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The impact of Hurricanes on the value of commercial real estate

The impact of Hurricanes on the value of commercial real estate Commercial real estate investors prefer coastal, gateway, markets for liquidity, demand density, and durable returns. Yet, these areas are more vulnerable to the effects of climate change from more intense and frequent weather events such as hurricanes and typhoons as well as to gradual changes such as sea-level rise. Recognition is growing of the risks that these events pose to investment performance, but little is known about how this risk has impacted property values and returns when an event such as a hurricane occurs. This is the first study to analyze the impact on property values and returns from hurricanes causing the most significant damage by value over the past 30-plus years throughout the nation. Using individual property data from the National Council of Real Estate Investment Fiduciaries database, we find a significant impact on the value and rates of return, after accounting for any additional capital expenditures for repairs, for properties that are in areas impacted by a hurricane, relative to areas that were not impacted by a hurricane. These impacts vary by property type and can last for several years after the hurricane hit land in the area. Keywords Real estate · Investment · Property risk · Hurricane · Climate change 1 Introduction to National Oceanic and Atmospheric Administration estimates. Investor preferences for coastal, gateway, markets mean that For leading real estate investors and investment manag- many assets held by real estate investors are in cities more ers, the need to understand and develop strategies to address vulnerable to the effects of climate change. These effects climate-related risks needs to be understood and prioritized. range from more intense and frequent weather events such Yet little is known about how this risk has impacted prop- as hurricanes and typhoons to gradual changes such as sea- erty values and returns when an event such as a hurricane level rise. Recognition is growing of the risks that these occurs. The impact extends beyond the direct damage to the events pose to investment performance. Recent weather property. An increase in the perceived risk of hurricanes events caused significant physical damages to properties can result in a reluctance by institutional investors to add and infrastructure. In 2017, the year Hurricanes Harvey and capital to that area, which can lead to a negative impact on Maria hit the United States and storms battered northern property values and returns well after the hurricane to all and central Europe, insurers paid out a record $135 billion properties and property types in the area that was impacted globally for damage caused by storms and natural disasters. by a hurricane. This is the first study to analyze the impact This figure does not represent actual damages, which in the on property values and returns from all the significant hur - United States alone which equaled $307 billion, according ricanes that occurred over the past 30-plus years throughout the nation. We find that there has been a significant impact on the value and rates of return, after accounting for any * Sara R. Rutledge additional capital expenditures for repairs, for properties that srrutledge@gmail.com are in areas impacted by a hurricane relative to areas that Jeffrey D. Fisher were not impacted by a hurricane. This can last for several jfishe48@jhu.edu years after the hurricane hit land in the area. Homer Hoyt Institute, 760 US Highway 1, Suite 300, North Palm Beach, FL 33408, USA SRR Consulting, 1620 Fowler Avenue, Evanston, IL 60201, USA Vol.:(0123456789) 130 J. D. Fisher, S. R. Rutledge largely related to recurring hurricane landfalls and increased 2 Literature review perceptions of catastrophic risk. Morgan (2007) also address the topic of risk perceptions The real estate literature is limited on this topic. There is before after Hurricane Ivan. The author’s focus was on research exploring the impact of natural disasters on resi- how subsidized insurance premiums could create a market dential property market activity and valuation. This research imbalance by reducing expected flood losses and perceived largely covers single-family housing with limited coverage risks associated with living in floodplain areas. In addition on multifamily properties. Although other commercial prop- to the findings on Santa Rosa County, Florida home prices erty types are rarely considered, these studies provide three declines after the hurricane, the author observed that dam- common themes: (1) natural disasters negatively impact real age from Ivan led to significant changes in flood insurance estate, (2) increased expectations of natural disasters amplify premiums. The research estimated pre- and post-hurricane this negative impact, and (3) negative real estate impacts capitalized values of flood insurance premiums to conclude fade over time. that expected flood losses in the county rose by 75% after The literature, as in Graham and Hall (2001), Bliech Ivan, amplifying the perceived risk of properties located in (2003), and Morgan (2007), establishes that natural disasters these floodplains. have a negative impact on real estate pricing and/or values. The importance to this study lies in the increasing fre- Graham and Hall (2001), in a study of multiple hurricanes quency of large and powerful hurricanes due to the chang- making landfall in the Wilmington area of North Carolina, ing global climate. Highlighting this shift in frequency and find that residential property values face increasingly nega- intensity is the fact that 14 of the top 20 costliest mainland tive impacts from successive hurricanes. Also relevant to United States tropical cyclones have occurred from 2000 to this paper is that Graham and Hall (2001) used factors in 2017, two of which made landfall in 2017—Harvey (tied their model to control for other impacts on housing values, with Katrina 2005 for costliest storm) and Irma (ranked such as location and economic conditions. fifth). Morgan (2007) researched Hurricane Ivan’s 2004 impacts Graham et al. (2007) expanded upon Graham and Hall’s on risk perceptions and housing values in Santa Rosa earlier research to study the duration of negative impacts County, Florida. The author identifies reduced home sale to housing values following landfall of a hurricane. The prices for properties in high-risk flood areas after the hur - authors’ findings support the same pattern of increasing ricane. Housing in these floodplains commanded a premium home price declines with each successive hurricane. Then, relative to other houses in the county prior to the hurricane, their research reveals a tempering of this trend with a price as federally insured areas in desirable waterfront locations. recovery and the return to pre-storm market stability over the It is noteworthy that the observed 15% post-Ivan reduc- 3 years following the last major hurricane strike. tion in prices did not eliminate the home price premium in Similarly, Salter and King (2009) found that an unan- floodplains. nounced event can cause an overreaction that the market Turning to a  different type of natural disaster, Bleich will correct via pricing adjustments, although informa- (2003) researched the impact of the 1994 Northridge Earth- tion inefficiencies can create a lag in this correction. Their quake on the Los Angeles multifamily property market. A research covered the post-Hurricane Katrina housing market negative impact on values was identified using capitalization in Hattiesburg, Mississippi—an area on the periphery of the rates, which rose overall. However, multifamily capitaliza- storm’s impact. This study of a market less damaged, but tion rates increased the most near the earthquake’s epicenter adjacent to more devastated areas, yields additional infor- and areas not experiencing damage did not have valuation mation about the complexity of hurricane impacts on real impacts. estate pricing and liquidity. The supply/demand balance for The Graham and Hall (2001) findings of increasingly neg- housing tightened due an increase in housing demand from ative impacts on housing values led the authors to conclude people displaced by the storm and a reduction, at least tem- that a period of unprecedented hurricane activity increased porarily, in supply from storm damage. Thus, the market the market expectations of catastrophic risk, contributing correction is related to the timing for bringing supply back to market instability. Housing in the Wilmington area of online through repairs or new construction. North Carolina was studied after landfall by hurricanes Ber- In the Bleich (2003) earthquake study, proximity to the tha and Fran in 1996, Bonnie 1998, and Floyd 1999. Results natural disaster was found to be a factor in the real estate revealed limited effects after the 1996 storms, but succes- sively more extreme and immediate negative consequences on real estate values were observed after Bonnie and Fran. This structural shift in the housing market was identified as National Hurricane Center, Costliest U.S. tropical cyclone tables updated, Table  3a. https:// www. nhc. noaa. gov/ news/ Updat edCos tliest. pdf. The impact of Hurricanes on the value of commercial real estate 131 effects with first year impacts correlated to the epicenter and were not impacted either during the same time as a hurri- areas with the high concentrations of damage. The author cane, or ever impacted by a hurricane, to capture the relative also found these impacts to be temporary because, by the impact of hurricanes on property values and returns. third year of the analysis, negative effects were not signifi- Nineteen hurricanes making U.S. landfall were included, cant. There were, however, lingering effects on values for as summarized in Table 1. The table lists the hurricane’s older multifamily buildings with architectural styles that name, landfall date, estimated damage, and impacted loca- proved to be less resistant to earthquakes. On the flipside, tions. The NHC’s most recent list of the costliest U.S. tropi- Simmons et al. (2002) find that risk mitigation factors in a cal cyclones (updated 2018) was used to identify a list of Gulf Coast city to protect homes from hurricane damage major hurricanes. The NHC damage estimates include enhanced home values. insured and uninsured losses and are estimated using This paper addresses all three themes by researching the source data from Federal Emergency Management Agency, impact on real estate values from the most catastrophic hur- U.S. Department of Agriculture, National Interagency Fire ricanes over the years after these storms. Most importantly, Center, U.S. Army Corps of Engineers, state emergency this paper fills a significant gap in the literature by examin- management agencies, state and regional climate centers, ing these impacts on commercial real estate values. and insurance industry estimates. This broad assessment of damages reveals a list of storms most likely to affect com- mercial real estate. The fifteenth costliest hurricane on the 3 Data NHC list (Allison) made landfall as a tropical storm. Given its ranking on the list and impact to a major real estate mar- The data for this study come from the National Council of ket (Houston), this storm was included in the analysis. Real Estate Investment Fiduciaries (NCREIF). NCREIF is The Census-defined core-based statistical areas (CBSAs) a non-profit, membership organization of the institutional and divisions impacted by these hurricanes were determined investment managers that invest in U.S. commercial real by reviewing detailed cyclone reports from the U.S. Depart- estate on behalf of their clients, including high net worth ment of Commerce National Oceanic and Atmospheric individuals, pension funds, and endowments. NCREIF was Administration National Hurricane Center (NHC). CBSAs formed to create benchmarks to track the performance of are U.S. geographic areas defined by the Office of Manage- commercial real estate as an asset class. ment and Budget, and consist of one or more counties (or The NCREIF Property Index (NPI) begins in 1978 and equivalents) anchored by an urban center of at least 10,000 includes quarterly data on five major property types: apart- people plus adjacent counties that are socioeconomically ment, hotel, industrial, office, and retail. The quarterly prop- tied to the urban center by commuting. Larger CBSAs by erty data are provided directly to NCREIF from the account- population may have multiple divisions within them. If a ing of individual property performance by their investment CBSA has a division, we use the division instead of the management membership. Data on income and capital entire CBSA. expenditures are provided on the properties each quarter in The CBSAs and divisions in this analysis were selected addition to appraised property values, because managers use based upon each hurricane’s tracked path from landfall until current value accounting for performance reporting to inves- the storm was no longer categorized as a hurricane per NHC tors. Investment manager members also report data on other reporting. In some cases, a CBSA or division identified as property types, such as self-storage and senior housing, and being impacted by a hurricane did not have property data these data are included in the complete property database in the NCREIF database and had to be excluded from this available for research use. As of fourth quarter 2019, the NPI analysis. includes 8262 properties with an aggregate market value of $658.4 billion, and the complete property database includes 10,213 properties valued at $741.2 billion. The historical 4 Methodology database has information on nearly 800,000 properties, including those that have been sold over time. This study is designed to capture the impact of a hurricane For this study, individual property data were used from on all the properties in CBSAs and/or divisions. We measure 1989 to 2019, which spans the period the major hurricanes the impact of a hurricane on a CBSA or division that had a occurred that were included in this study. Property types included office, retail, apartment, industrial, and hotel, span- ning the entire U.S. There were over 400,000 property obser- Locations impacted by hurricanes without corresponding NCREIF vations (cross sectional and time series) depending on the data include: Dover, DE (Hurricane Isabel), Lafayette, LA (Hurricane panel regression used, as discussed below. The data cover Gustav), Lake Charles, LA (Hurricanes Harvey and Rita), and More- both areas that were impacted by hurricanes and areas that head City, NC (Hurricanes Irene and Floyd). 132 J. D. Fisher, S. R. Rutledge Table 1 Major U.S. Hurricanes Hurricane name U.S. Landfall quarter NHC damage est. Impacted CBSAs and divisions (billions, nominal) Allison 2Q 2001 $8.5 Houston-The Woodlands, TX Andrew 3Q 1992 $27.0 Baton Rouge, LA New Orleans, LA Fort Lauderdale, FL West Palm Beach, FL Miami, FL Charley 3Q 2004 $16.0 Daytona Beach, FL Orlando, FL Fort Meyers, FL Tampa, FL Myrtle Beach, SC Floyd 3Q 1999 $6.5 Virginia Beach-Norfolk, VA Wilmington, NC Fran 3Q 1996 $5.0 Myrtle Beach, SC Washington, DC Raleigh-Durham, NC Wilmington, DE Frances 3Q 2004 $9.8 Daytona Beach, FL Tampa, FL Orlando, FL West Palm Beach, FL Port St. Lucie, FL Gustav 3Q 2008 $6.0 Baton Rouge, LA New Orleans, LA Harvey 3Q 2017 $125.0 Houston-The Woodlands, TX Corpus Christie, TX Beaumont-Port Arthur, TX Victoria-Port Lavaca, TX Hugo 3Q 1989 $7.0 Charleston, SC Columbia, SC Charlotte, NC Myrtle Beach, SC Ike 3Q 2008 $30.0 Houston-The Woodlands, TX Beaumont-Port Arthur, TX Irene 3Q 2011 $13.5 Atlantic City, NJ New York, NY Jacksonville, NC Virginia Beach-Nor- Nassau Co-Suffolk Co, NY folk, VA Irma 3Q 2017 $50.0 Fort Lauderdale, FL Orlando, FL Fort Meyers, FL Port St. Lucie, FL Gainesville, FL Savannah, GA Miami, FL Tampa, FL Naples, FL West Palm Beach, FL Isabel 3Q 2003 $5.5 Baltimore, MD Washington, DC Virginia Beach-Norfolk, VA Wilmington, DE Jeanne 3Q 2004 $7.5 Daytona Beach, FL Tampa, FL Orlando, FL West Palm Beach, FL Port St. Lucie, FL Katrina 3Q 2005 $125.0 New Orleans, LA Gulfport, MS Matthew 4Q 2016 $10.0 Charleston, SC Myrtle Beach, SC Savannah, GA Hilton Head, SC Wilmington, NC Jacksonville, FL Jacksonville, NC Rita 3Q 2005 $18.5 Houston-The Woodlands, TX Beaumont-Port Arthur, TX Sandy 4Q 2012 $65.0 Atlantic City, NJ New York, NY Camden, NJ Newark, NJ Nassau Co-Suffolk Co, NY Ocean City, NJ Wilma 4Q 2005 $19.0 Fort Lauderdale, FL Miami, FL Fort Meyers, FL West Palm Beach, FL hurricane whether it was physically damaged or not. Insti- values after a hurricane—especially if the risk of future hur- tutional investors can choose to allocate less or no capital ricanes is perceived to have increased due to climate change. to areas that have been impacted by a hurricane and tenants Panel regression using cross-sectional and time-series can be more reluctant to sign leases in those areas. All these data methodology were estimated. For every property and factors can impact occupancy, risk premiums and property for every quarter, we calculated the cumulative change in The impact of Hurricanes on the value of commercial real estate 133 Table 2 Variable summary Variable Type Description Property value Dependent Quarterly appraisal-based property market value per NCREIF Property total return Dependent Quarterly property investment return from income and appreciation per NCREIF Property capital return Dependent Quarterly property return from market appreciation per NCREIF HurricaneQtr Dummy Indicator is 1 for properties in a location (CBSA or Division) impacted by a major hurricane in during the quarter CBSAorDiv Dummy Dummy variables for all locations (CBSA or Division) to control for fixed effects yyyyq Dummy Dummy variables for each quarterly period to control for property market conditions over time Square feet (sqft) Independent Property size in square feet per NCREIF Sqft2 Independent Squared property size to allow for nonlinear relationship to property performance Age Independent Property age in quarters from completion date per NCREIF Age2 Independent Squared property age in quarters to allow for nonlinear relationship to property performance Percentleased Independent Leased square feet in a property as a share of the property’s total square feet for the quarter before the hurricane Apartmenthq Interaction Interaction dummy variable with an indicator of 1 for apartment properties in a hurricane quarter Industrialhq Interaction Interaction dummy variable with an indicator of 1 for industrial properties in a hurricane quarter Officehq Interaction Interaction dummy variable with an indicator of 1 for office properties in a hurricane quarter Retailhq Interaction Interaction dummy variable with an indicator of 1 for retail properties in a hurricane quarter value and cumulative return over the next 1, 2, 3, 4, and To control for the fixed effects of different locations in 5-year periods. This process creates the dependent variables the U.S., dummy variables were also created for each CBSA that are used in the various models. For example, one model or division regardless of whether it was impacted by a hur- will examine how the value changed over the four quarters ricane. These are strictly cross-sectional dummy variables. after the hurricane landfall quarter. This change in value Similarly, we created dummy variables for each quarter will be calculated for all properties whether they were in the to control for changes in market conditions over time. The CBSA or division impacted by the hurricane or not so we coefficients of these quarterly dummy variables could be can compare the relative change in value. used to create a national price index. As a check on the Similar dependent variables were created for the capital validity of the model, we verified that this price index return (or appreciation), which is a measure of the change essentially replicated the equal weighted version of the in value net of capital expenditures (capex) and for the total NCREIF Property Index. return, which is the combination of income and capital returns. Appreciation by itself was used in addition to the change in value because properties impacted by hurricanes might have incurred more capex for repairs than properties Table 3 Regression results for cumulative property value change, 8 quarters after hurricane not impacted by a hurricane. This allows us to consider that some of the loss in value from the hurricane may have been Variable Coefficient Stnd. error t-stat restored by additional capex being spent on the property. Constant 0.12153350 0.07604170 1.60 To determine whether a hurricane impacted the value Sqft 0.00000001 0.00000000 9.07 change and other measures discussed above after the hurri- Sqft2 0.00000000 0.00000000 − 6.09 cane, a dummy variable was used to indicate if the property Age 0.00034200 0.00003520 9.72 is in the CBSA or division impacted by one of the hurricanes Age2 − 0.00000015 0.00000002 − 8.47 during the quarter of the hurricane. The dummy variable Percentleased 0.05253790 0.00391590 13.42 is 1 if the property is in the CBSA or division where there HurricaneQtr − 0.25885300 0.07441150 − 3.48 was a hurricane during that quarter. Otherwise, the dummy Observations 334,132 variable was zero. Thus, the coefficient of this variable indi - MSE 0.09697279 cates the marginal impact of a property being in the area F test (probability) 0.00000000 of the hurricane in the quarters after the occurrence of the hurricane. 134 J. D. Fisher, S. R. Rutledge 5% Figure 1 Cumulative property 0.4% value change for quarters after 0% hurricanes -5% -10% -14.0% -15% -22.0% -20% -25.0% -25% -30% -35% -40% -45.0% -45% -50% 4Q 8Q 12Q16Q 20Q variable indicates how each property type was impacted Table 4 Regression results for cumulative property value change with property-type interaction terms, 8 quarters after hurricane relative to the impact on hotels. The variables described in this section are summarized Variable Coefficient Stnd. error t-stat in Table 2 above. Regressions were run separately for each Constant 0.12376260 0.07602000 1.63 of the time periods after the hurricane (1 year after, 2 years Sqft 0.00000001 0.00000000 8.93 after, etc.) and for each of the different dependent variables Sqft2 0.00000000 0.00000000 − 6.05 (price change, capital return, and total return). The results Age 0.00035140 0.00003520 9.97 are discussed in the next section. Age, squared − 0.00000015 0.00000002 − 8.79 Percent leased 0.04967120 0.00393010 12.64 HurricaneQtr − 0.30781210 0.07508430 − 4.10 5 Results Apartmenthq 0.04246640 0.01064470 3.99 Industrialhq 0.07234580 0.01050950 6.88 Table 3 shows the results of one of the regressions of the Officehq 0.03552060 0.01061390 3.35 impact of hurricanes on the cumulative change in value eight Retailhq 0.04477270 0.01074850 4.17 quarters after the hurricane. There were 334,132 property- Observations 334,132 quarter observations. The cofficients on the dummy vari - MSE 0.09697279 ables used for each location (CBSA or Division) and quarter F test (probability) 0.00000000 are not shown below, but are provided in the Appendix. The estimated coefficients on the variables for square feet, square feet squared, age, and age squared are all highly Independent variables were also used to control for the significant, as is the occupancy at the time of the hurricane. fact that the change in value, and returns for a property The hurricane dummy variable indicates that the property tend to vary with the size (measured in square feet) and was in the CBSA or division impacted by a hurricane on age (measured in quarters from property completion date) the quarter the hurricane made U.S. landfall. It indicates of the property. These impacts tend to be nonlinear. Thus, how much this affected the cumulative change in value over we included variables for the property square footage, the following eight quarters relative to how the properties square footage squared, property age, and age squared. If performed that were not in the areas impacted by the hur- the relationship turned out to be linear, the coefficients of ricane. In this model, the property-type interaction vari- these squared variables would be insignificant. We also ables are omitted so we can get an indication of the overall included the occupancy of each property as of the quarter impact on a portfolio of all property types. The results sug- prior to the hurricane as an independent variable. gest that over the eight quarters following the hurricane Finally, we created interaction dummy variables to quarter, property values increased by 25.9% less than prop- indicate what the property type is for a property that was erties not impacted by a hurricane, or 3.2% per quarter. impacted by a hurricane. For example, the office interac- The same regression was run for 1, 3, 4, and 5-year tion dummy variable was 1 if it was an office property time periods following hurricane landfall for all prop- with the hurricane dummy of 1 in the area of a hurricane erty types combined. Figure 1 is a graph of the impact on as of the quarter of a hurricane. Hotel properties were the value change over time. We see that the impact on value omitted dummy variable. The coefficient of this dummy The impact of Hurricanes on the value of commercial real estate 135 Table 5 Cumulative value change by property type, from 1 to 5 years Table 6 Cumulative appreciation change by property type, from 1 to after hurricane 5 years after hurricane Property type Quarters after Hurricane Property type Quarters after Hurricane 4Q (%) 8Q (%) 12Q (%) 16Q (%) 20Q (%) 4Q (%) 8Q (%) 12Q (%) 16Q (%) 20Q (%) Hotel − 14.5 − 30.0 − 41.0 − 21.0 − 2.0 Hotel − 11.0 − 27.3 − 43.3 − 24.0 − 7.4 Apartment − 13.8 − 26.0 − 41.7 − 19.0 5.0 Apartment − 7.1 − 24.9 − 39.8 − 14.2 6.2 Industrial − 12.4 − 23.0 − 46.0 − 23.0 − 1.6 Industrial − 8.4 − 24.9 − 46.2 − 20.4 − 3.4 Office − 15.5 − 26.5 − 45.0 − 18.0 − 3.5 Office − 9.9 − 27.8 − 47.3 − 25.1 − 7.4 Retail − 13.0 − 25.5 − 42.0 − 19.0 5.0 Retail − 8.7 − 27.4 − 41.9 − 16.3 5.5 Figure 2 Cumulative value 10% change for quarters after hur- ricanes by property type 0% -10% -20% -30% -40% -50% 4Q 8Q 12Q16Q 20Q Office Retail Apartment Industrial Hotel continues to be negative until 3 years (or 12 quarters) the total impact is still negative when the coefficient for the after the hurricane, and then, the impact starts to dissi- interaction dummy is added to the coefficient of the hurricane pate with values recovering 5 years (or 20 quarters) after quarter dummy. Table 5 shows the results when regressions are the hurricane. run for 1 to 5 years, and Fig. 2 graphs the results. Table 4 shows the regression results when the property- We have shown the impact of hurricanes on the cumula- type interaction dummies are added. For example, the apart- tive change in value after the ocurrance of a hurricane on ment dummy is 1 if there is a hurricane during the quarter each property type. This is the impact on all properties in the in that CBSA or division and the property is an apartment. area of the hurricane regardless of whether they were actu- The impact of a hurricane is then found by adding the coef- ally physically damaged. Those properties that were physi- ficient from the hurricane quarter dummy to the interaction cally damaged may have had repairs after the hurricane that dummy for each property type. Since hotels are the omitted would restore the loss in value due to the damage—but not property-type interaction variable, the impact on hotels is any impact due to less demand by tenants and investors for just the coefficient of the hurricane quarter dummy. properties in the hurricane impacted area. The hurricane quarter dummy variable (as well as each of To control for the capital expenditure on repairs, we cal- the property-type interaction variables) is statistically signifi- culated the cumulative capital return for each property for 1, cant. The positive coefficients for the property-type dummies 2, 3, 4, and 5 years after the hurricane. The capital return is indicate that the impact of the hurricane is less for that property the change in value net of capital expenditures. That is, if the type than for the omitted hotel interaction variable. However, value increased only because of capital expenditures, the capi- tal return would be zero. The regressions discussed above were repeated using the cumulative capital return as the dependent An alternative approach is to leave out the hurricane quarter variable. The results are shown in Table 6 and Fig. 3. dummy when including the property-type interaction variables. In this case, the property-type interaction variables would capture the Finally, we examined the impact of hurricanes on the total full impact of the hurricane on that property type. There would be return that investors would receive from income and capital no need to leave out a property type because the “omitted variable” appreciation net of capex. In this case, the coefficients indi- would be when there is no hurricane that quarter in a CBSA or divi- cate how much the return is impacted relative to the NCREIF sion. Using this approach resulted in essentially the same results for the impact of the hurricane on each property type. Property Index (NPI) in areas not impacted by a hurricane. 136 J. D. Fisher, S. R. Rutledge Figure 3 Cumulative apprecia- 10% tion change for quarters after 0% hurricanes by property type -10% -20% -30% -40% -50% 4Q 8Q 12Q16Q 20Q Office Retail Apartment Industrial Hotel Table 7 Cumulative change in total return by property type, from 1 to since 1988, which include 19 storms that impacted differ - 5 years after hurricane ent areas of the U.S. After controlling for property size, age, location, time (market conditions), and occupancy, Property type Quarters after Hurricane we find that hurricanes appear to have a significant impact 4Q (%) 8Q (%) 12Q (%) 16Q (%) 20Q (%) on property values, appreciation (net of capex), and total Hotel − 5.0 − 24.5 − 43.8 − 7.0 5.3 return. Apartment − 6.1 − 31.5 − 52.2 − 11.0 4.3 The impact on all three measures peaked 3 years after Industrial − 5.9 − 28.5 − 54.2 − 12.0 − 0.7 hurricane landfall and then began to dissipate over the fol- Office − 7.4 − 32.0 − 56.8 − 19.4 − 6.7 lowing 2 years. Five years after a major hurricane, apartment Retail − 6.7 − 32.5 − 51.7 − 9.4 7.3 and retail properties had recovered, but office, hotel, and industrial still experienced a cumulative negative impact on the capital return. The results of this study are important for investors decid- The cumulative return was calculated from 1 to 5 years after the hurricane. The results are shown in Table 7 and Fig. 4. ing whether to allocate additional capital—especially if the perceived risk of additional hurricanes in an area is increas- ing due to climate change. The impact on property values and returns that we found go beyond any impact due to phys- 6 Conclusion ical damage to the properties. The loss in value appears to last up to 5 years after the hurricane makes landfall, and This paper examined the impact of hurricanes on proper- ties owned by institutional investors. It is the first study to is likely a result of higher risk premiums and lower tenant demand after the occurrence of a hurricane. examine all the significant hurricanes that have occurred Figure 4 Cumulative total 20% return change for quarters after 10% Hurricanes by property type 0% -10% -20% -30% -40% -50% -60% 4Q 8Q 12Q16Q 20Q Office Retail Apartment Industrial Hotel The impact of Hurricanes on the value of commercial real estate 137 Appendix Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) able Quarter and Location Dummy Variable Regression Results, yyyyq_19964 − 0.05524410 − 0.75 − 0.05545580 − 0.75 without (1) and with (2) Interaction Dummy Variables yyyyq_19971 − 0.05735300 − 0.78 − 0.05754380 − 0.78 yyyyq_19972 − 0.06629550 − 0.90 − 0.06648180 − 0.91 Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) yyyyq_19973 − 0.07319480 − 1.00 − 0.07338530 − 1.00 able yyyyq_19974 − 0.09193420 − 1.25 − 0.09210540 − 1.26 yyyyq_19852 0.00562370 0.06 0.00568200 0.06 yyyyq_19981 − 0.09811930 − 1.34 − 0.09827660 − 1.34 yyyyq_19853 0.01743420 0.17 0.01750870 0.17 yyyyq_19982 − 0.10344660 − 1.41 − 0.10361530 − 1.41 yyyyq_19854 − 0.03822030 − 0.38 − 0.03812980 − 0.38 yyyyq_19983 − 0.10550190 − 1.44 − 0.10566160 − 1.44 yyyyq_19861 − 0.07669100 − 0.78 − 0.07665380 − 0.78 yyyyq_19984 − 0.11681660 − 1.59 − 0.11696840 − 1.60 yyyyq_19862 − 0.06286220 − 0.66 − 0.06265060 − 0.66 yyyyq_19991 − 0.12166000 − 1.66 − 0.12183820 − 1.66 yyyyq_19863 − 0.05191480 − 0.56 − 0.05174670 − 0.56 yyyyq_19992 − 0.12534610 − 1.71 − 0.12551910 − 1.71 yyyyq_19864 − 0.02928690 − 0.32 − 0.02921820 − 0.32 yyyyq_19993 0.11601060 7.20 0.11622430 7.22 yyyyq_19871 − 0.10424150 − 1.36 − 0.10441380 − 1.36 yyyyq_19994 − 0.16960610 − 2.31 − 0.16978330 − 2.32 yyyyq_19872 − 0.09822290 − 1.29 − 0.09840280 − 1.29 yyyyq_20001 − 0.17470770 − 2.38 − 0.17487810 − 2.39 yyyyq_19873 − 0.09941110 − 1.30 − 0.09958360 − 1.30 yyyyq_20002 − 0.19271280 − 2.63 − 0.19286580 − 2.63 yyyyq_19874 − 0.11032860 − 1.45 − 0.11055000 − 1.45 yyyyq_20003 − 0.20101940 − 2.74 − 0.20116310 − 2.75 yyyyq_19881 − 0.14758310 − 1.98 − 0.14778930 − 1.99 yyyyq_20004 − 0.22048120 − 3.01 − 0.22063380 − 3.01 yyyyq_19882 − 0.15339120 − 2.07 − 0.15360660 − 2.07 yyyyq_20011 − 0.23665740 − 3.23 − 0.23681730 − 3.23 yyyyq_19883 − 0.16924690 − 2.28 − 0.16946530 − 2.28 yyyyq_20012 0.02847760 1.81 0.02858290 1.81 yyyyq_19884 − 0.21615170 − 2.91 − 0.21639850 − 2.91 yyyyq_20013 − 0.24567900 − 3.36 − 0.24583230 − 3.36 yyyyq_19891 − 0.23314630 − 3.14 − 0.23338880 − 3.14 yyyyq_20014 − 0.24292020 − 3.32 − 0.24308070 − 3.32 yyyyq_19892 − 0.24192040 − 3.25 − 0.24216700 − 3.25 yyyyq_20021 − 0.22537760 − 3.08 − 0.22556240 − 3.08 yyyyq_19894 − 0.32476270 − 4.37 − 0.32498710 − 4.38 yyyyq_20022 − 0.20951310 − 2.86 − 0.20973680 − 2.87 yyyyq_19901 − 0.33985180 − 4.59 − 0.34009540 − 4.60 yyyyq_20023 − 0.18865390 − 2.58 − 0.18887910 − 2.58 yyyyq_19902 − 0.36513480 − 4.93 − 0.36537880 − 4.94 yyyyq_20024 − 0.19805760 − 2.71 − 0.19830570 − 2.71 yyyyq_19903 − 0.37653540 − 5.09 − 0.37674520 − 5.10 yyyyq_20031 − 0.13908620 − 1.90 − 0.13933310 − 1.90 yyyyq_19904 − 0.38840840 − 5.26 − 0.38866490 − 5.26 yyyyq_20032 − 0.18820120 − 2.57 − 0.18844380 − 2.58 yyyyq_19911 − 0.38006220 − 5.15 − 0.38031870 − 5.15 yyyyq_20033 0.13966830 8.94 0.13677690 8.76 yyyyq_19912 − 0.38211360 − 5.18 − 0.38234440 − 5.19 yyyyq_20034 − 0.09703900 − 1.33 − 0.09726360 − 1.33 yyyyq_19913 − 0.36916340 − 5.01 − 0.36938500 − 5.01 yyyyq_20041 − 0.04389540 − 0.60 − 0.04412190 − 0.60 yyyyq_19914 − 0.32689450 − 4.43 − 0.32712330 − 4.44 yyyyq_20042 − 0.02613820 − 0.36 − 0.02634300 − 0.36 yyyyq_19921 − 0.31761600 − 4.30 − 0.31787370 − 4.31 yyyyq_20043 0.24147530 16.18 0.23826680 15.96 yyyyq_19922 − 0.28782890 − 3.91 − 0.28805340 − 3.91 yyyyq_20044 − 0.02147050 − 0.29 − 0.02163840 − 0.30 yyyyq_19923 − 0.00036270 − 0.02 − 0.00250760 − 0.14 yyyyq_20051 − 0.00318710 − 0.04 − 0.00335910 − 0.05 yyyyq_19924 − 0.21314360 − 2.90 − 0.21338870 − 2.90 yyyyq_20052 0.00280650 0.04 0.00269800 0.04 yyyyq_19931 − 0.20243990 − 2.75 − 0.20267600 − 2.76 yyyyq_20053 0.24307570 16.21 0.23842970 15.90 yyyyq_19932 − 0.17757460 − 2.41 − 0.17781810 − 2.42 yyyyq_20054 0.22614080 14.69 0.22146830 14.38 yyyyq_19933 − 0.16859040 − 2.29 − 0.16882150 − 2.30 yyyyq_20061 − 0.04951130 − 0.68 − 0.04963050 − 0.68 yyyyq_19934 − 0.15931240 − 2.17 − 0.15954960 − 2.17 yyyyq_20062 − 0.08639170 − 1.18 − 0.08649160 − 1.18 yyyyq_19941 − 0.15271660 − 2.08 − 0.15293420 − 2.08 yyyyq_20063 − 0.12472230 − 1.71 − 0.12482910 − 1.71 yyyyq_19942 − 0.14924980 − 2.03 − 0.14942790 − 2.03 yyyyq_20064 − 0.24055330 − 3.29 − 0.24066160 − 3.29 yyyyq_19943 − 0.13319260 − 1.81 − 0.13335410 − 1.82 yyyyq_20071 − 0.33102210 − 4.53 − 0.33114730 − 4.53 yyyyq_19944 − 0.12889990 − 1.75 − 0.12904220 − 1.76 yyyyq_20072 − 0.41346090 − 5.66 − 0.41359250 − 5.66 yyyyq_19951 − 0.13109730 − 1.78 − 0.13124270 − 1.79 yyyyq_20073 − 0.47112360 − 6.44 − 0.47126680 − 6.45 yyyyq_19952 − 0.12176330 − 1.66 − 0.12190880 − 1.66 yyyyq_20074 − 0.50349110 − 6.89 − 0.50364190 − 6.89 yyyyq_19953 − 0.10507050 − 1.43 − 0.10521510 − 1.43 yyyyq_20081 − 0.50934650 − 6.97 − 0.50950560 − 6.97 yyyyq_19954 − 0.07552310 − 1.03 − 0.07570550 − 1.03 yyyyq_20082 − 0.50359730 − 6.89 − 0.50376680 − 6.89 yyyyq_19961 − 0.06993230 − 0.95 − 0.07014840 − 0.95 yyyyq_20083 − 0.22405520 − 15.09 − 0.22685090 − 15.28 yyyyq_19962 − 0.05570120 − 0.76 − 0.05590950 − 0.76 yyyyq_20084 − 0.40210170 − 5.50 − 0.40231170 − 5.51 yyyyq_19963 0.19748380 11.60 0.19621510 11.52 yyyyq_20091 − 0.32388370 − 4.43 − 0.32411030 − 4.43 138 J. D. Fisher, S. R. Rutledge Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) able able yyyyq_20092 − 0.22302120 − 3.05 − 0.22326680 − 3.05 CBSAor- 0.03630710 1.38 0.03803020 1.45 Div_11460 yyyyq_20093 − 0.17099110 − 2.34 − 0.17125030 − 2.34 CBSAor- 0.12314920 2.33 0.12465180 2.36 yyyyq_20094 − 0.11369210 − 1.56 − 0.11396770 − 1.56 Div_11700 yyyyq_20101 − 0.10318210 − 1.41 − 0.10352150 − 1.42 CBSAor- 0.06432920 0.59 0.06539830 0.60 yyyyq_20102 − 0.07778580 − 1.06 − 0.07813050 − 1.07 Div_11820 yyyyq_20103 − 0.10807030 − 1.48 − 0.10842040 − 1.48 CBSAor- − 0.00727200 − 0.14 − 0.00642160 − 0.12 yyyyq_20104 − 0.12864170 − 1.76 − 0.12898990 − 1.76 Div_12020 yyyyq_20111 − 0.13639330 − 1.87 − 0.13679160 − 1.87 CBSAor- 0.01238940 0.59 0.01199300 0.57 yyyyq_20112 − 0.13942410 − 1.91 − 0.13979420 − 1.91 Div_12060 yyyyq_20113 0.12938080 8.50 0.12793970 8.41 CBSAor- − 0.02782970 − 0.45 − 0.03054300 − 0.49 Div_12140 yyyyq_20114 − 0.15065190 − 2.06 − 0.15098020 − 2.07 CBSAor- 0.00310030 0.04 0.00591370 0.08 yyyyq_20121 − 0.13481750 − 1.84 − 0.13509460 − 1.85 Div_12220 yyyyq_20122 − 0.12937730 − 1.77 − 0.12960170 − 1.77 CBSAor- − 0.03616970 − 0.94 − 0.03433420 − 0.89 yyyyq_20123 − 0.13846560 − 1.89 − 0.13870390 − 1.90 Div_12260 yyyyq_20124 0.12163220 8.03 0.11938350 7.88 CBSAor- 0.05425160 0.80 0.05724100 0.84 yyyyq_20131 − 0.10180570 − 1.39 − 0.10202480 − 1.40 Div_12300 yyyyq_20132 − 0.09682420 − 1.32 − 0.09703950 − 1.33 CBSAor- 0.04505750 2.12 0.04617740 2.17 yyyyq_20133 − 0.09941950 − 1.36 − 0.09961560 − 1.36 Div_12420 yyyyq_20134 − 0.11104380 − 1.52 − 0.11123710 − 1.52 CBSAor- 0.02056440 0.65 0.02275120 0.72 Div_12540 yyyyq_20141 − 0.08588600 − 1.17 − 0.08608080 − 1.18 CBSAor- 0.02782890 1.30 0.02776190 1.30 yyyyq_20142 − 0.09618470 − 1.32 − 0.09634630 − 1.32 Div_12580 yyyyq_20143 − 0.11351450 − 1.55 − 0.11364350 − 1.56 CBSAor- − 0.03350870 − 0.33 − 0.03305450 − 0.32 yyyyq_20144 − 0.12322040 − 1.69 − 0.12336310 − 1.69 Div_12620 yyyyq_20151 − 0.16607000 − 2.27 − 0.16621280 − 2.27 CBSAor- − 0.01949730 − 0.52 − 0.01708590 − 0.45 yyyyq_20152 − 0.12684650 − 1.74 − 0.12698750 − 1.74 Div_12700 yyyyq_20153 − 0.14955400 − 2.05 − 0.14967780 − 2.05 CBSAor- 0.13639550 2.18 0.13366010 2.14 yyyyq_20154 − 0.15901450 − 2.18 − 0.15883410 − 2.17 Div_12860 yyyyq_20161 − 0.14654260 − 2.00 − 0.14637490 − 2.00 CBSAor- − 0.01246580 − 0.44 − 0.01065380 − 0.38 Div_12940 yyyyq_20162 − 0.14285090 − 1.95 − 0.14267860 − 1.95 CBSAor- − 0.38808790 − 4.02 − 0.38700080 − 4.01 yyyyq_20163 − 0.14493320 − 1.98 − 0.14475330 − 1.98 Div_13140 yyyyq_20164 0.10729300 7.12 0.10197400 6.77 CBSAor- 0.11265990 1.77 0.11392330 1.79 yyyyq_20171 − 0.14266010 − 1.95 − 0.14251270 − 1.95 Div_13300 yyyyq_20172 − 0.14273490 − 1.95 − 0.14256780 − 1.95 CBSAor- − 0.20068680 − 1.22 − 0.19881110 − 1.21 yyyyq_20173 0.10764440 7.26 0.10262960 6.92 Div_13380 CBSAor- 0.08967670 1.80 0.09062860 1.82 CBSAor- − 0.40636040 − 4.82 − 0.40553000 − 4.81 Div_10500 Div_13780 CBSAor- − 0.08396940 − 2.13 − 0.08340060 − 2.12 CBSAor- − 0.01877840 − 0.74 − 0.01665950 − 0.66 Div_10580 Div_13820 CBSAor- − 0.02741100 − 1.09 − 0.02579920 − 1.03 CBSAor- 0.05279690 0.88 0.05385080 0.89 Div_10740 Div_13980 CBSAor- 0.05636630 2.31 0.05489380 2.25 CBSAor- − 0.00011130 0.00 0.00068930 0.02 Div_10900 Div_14010 CBSAor- − 0.09550510 − 1.33 − 0.09376300 − 1.31 CBSAor- − 0.07013260 − 0.92 − 0.06860190 − 0.90 Div_11020 Div_14020 CBSAor- − 0.11377710 − 2.56 − 0.11245190 − 2.53 CBSAor- 0.03582320 0.54 0.03773570 0.57 Div_11100 Div_14100 CBSAor- − 0.01318390 − 0.07 − 0.01252100 − 0.06 CBSAor- 0.00875670 0.15 0.01053660 0.19 Div_11180 Div_14260 CBSAor- 0.05509590 2.60 0.05525730 2.61 CBSAor- 0.03990870 1.86 0.04158780 1.94 Div_11244 Div_14454 CBSAor- − 0.00872410 − 0.31 − 0.00736510 − 0.26 Div_11260 The impact of Hurricanes on the value of commercial real estate 139 Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) able able CBSAor- 0.01384760 0.62 0.01343010 0.60 CBSAor- − 0.05582420 − 2.30 − 0.05413760 − 2.23 Div_14500 Div_17460 CBSAor- 0.29152510 1.77 0.27989230 1.70 CBSAor- 0.06682720 1.15 0.06857920 1.18 Div_14540 Div_17660 CBSAor- 0.01029260 0.45 0.01309990 0.57 CBSAor- − 0.04524050 − 0.99 − 0.04245070 − 0.93 Div_14860 Div_17780 CBSAor- − 0.08061200 − 1.43 − 0.08074740 − 1.43 CBSAor- 0.01952470 0.80 0.02175420 0.90 Div_15180 Div_17820 CBSAor- − 0.13339490 − 1.45 − 0.13295800 − 1.45 CBSAor- − 0.01106770 − 0.22 − 0.00843200 − 0.17 Div_15260 Div_17860 CBSAor- − 0.13551330 − 3.28 − 0.13490930 − 3.26 CBSAor- 0.02982490 1.15 0.03077250 1.19 Div_15380 Div_17900 CBSAor- − 0.21293390 − 2.32 − 0.21188710 − 2.31 CBSAor- − 0.14734160 − 1.94 − 0.14332160 − 1.89 Div_15460 Div_17980 CBSAor- 0.03020140 0.48 0.03120030 0.50 CBSAor- − 0.03751700 − 1.71 − 0.03623940 − 1.65 Div_15660 Div_18140 CBSAor- − 0.06368730 − 1.14 − 0.06798280 − 1.22 CBSAor- − 0.12806460 − 2.40 − 0.12453000 − 2.33 Div_15680 Div_18180 CBSAor- 0.01487390 0.70 0.01653080 0.77 CBSAor- − 0.10770200 − 0.54 − 0.10701360 − 0.53 Div_15764 Div_18420 CBSAor- 0.02686060 1.22 0.02632430 1.19 CBSAor- 0.06517690 2.21 0.06647750 2.26 Div_15804 Div_18580 CBSAor- − 0.11706110 − 1.44 − 0.12072400 − 1.49 CBSAor- − 0.08362120 − 1.48 − 0.08269440 − 1.46 Div_15820 Div_18880 CBSAor- − 0.08229190 − 0.90 − 0.08127590 − 0.89 CBSAor- 0.00838220 0.13 0.00929930 0.15 Div_15940 Div_18900 CBSAor- 0.04378260 1.83 0.04517490 1.89 CBSAor- 0.01901320 0.90 0.01899260 0.90 Div_15980 Div_19124 CBSAor- − 0.50701390 − 4.66 − 0.50643830 − 4.65 CBSAor- − 0.10346700 − 1.62 − 0.10246010 − 1.61 Div_16060 Div_19140 CBSAor- − 0.15942930 − 2.03 − 0.15785000 − 2.01 CBSAor- 0.00291180 0.04 0.00369330 0.06 Div_16180 Div_19220 CBSAor- − 0.08494010 − 0.97 − 0.08435320 − 0.96 CBSAor- − 0.07833080 − 1.92 − 0.07756310 − 1.90 Div_16300 Div_19340 CBSAor- 0.04296480 0.84 0.04053850 0.80 CBSAor- − 0.01826220 − 0.52 − 0.01701250 − 0.48 Div_16540 Div_19380 CBSAor- − 0.04801020 − 0.72 − 0.04734270 − 0.71 CBSAor- 0.01064810 0.26 0.00908340 0.22 Div_16580 Div_19500 CBSAor- 0.06947770 2.52 0.07144950 2.59 CBSAor- 0.04641600 1.39 0.04804730 1.44 Div_16700 Div_19660 CBSAor- 0.01860070 0.86 0.01963120 0.91 CBSAor- 0.04525330 2.13 0.04674530 2.20 Div_16740 Div_19740 CBSAor- − 0.01645690 − 0.46 − 0.01317210 − 0.37 CBSAor- − 0.07783360 − 2.35 − 0.07648890 − 2.31 Div_16820 Div_19780 CBSAor- 0.09295770 1.64 0.09378300 1.66 CBSAor- 0.03239320 1.41 0.03166430 1.38 Div_16860 Div_19804 CBSAor- 0.01041280 0.49 0.01039410 0.49 CBSAor- 0.05819300 0.63 0.05849880 0.64 Div_16974 Div_20260 CBSAor- − 0.17558810 − 2.38 − 0.17617770 − 2.39 CBSAor- 0.03996940 1.74 0.04107220 1.79 Div_17020 Div_20500 CBSAor- − 0.02496500 − 1.14 − 0.02533920 − 1.16 CBSAor- − 0.03546880 − 0.79 − 0.02996150 − 0.66 Div_17140 Div_20524 CBSAor- − 0.11153780 − 0.56 − 0.11087520 − 0.55 CBSAor- 0.00498100 0.11 0.01545490 0.35 Div_17200 Div_20780 140 J. D. Fisher, S. R. Rutledge Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) able able CBSAor- − 0.01872830 − 0.29 − 0.01771620 − 0.28 CBSAor- 0.10069130 1.04 0.10149390 1.05 Div_20900 Div_24220 CBSAor- − 0.01737660 − 0.74 − 0.01839430 − 0.78 CBSAor- 0.03257670 0.42 0.03341970 0.43 Div_20994 Div_24300 CBSAor- − 0.01421310 − 0.47 − 0.01473380 − 0.49 CBSAor- 0.00993710 0.25 0.01191070 0.31 Div_21340 Div_24340 CBSAor- − 0.31240480 − 5.84 − 0.31258560 − 5.85 CBSAor- 0.01709620 0.27 0.01849030 0.29 Div_21420 Div_24540 CBSAor- 0.25771070 1.28 0.25717690 1.28 CBSAor- 0.01053820 0.42 0.01212440 0.48 Div_21460 Div_24660 CBSAor- 0.02442750 0.60 0.02520400 0.62 CBSAor- 0.06961350 0.89 0.07231600 0.92 Div_21660 Div_24780 CBSAor- − 0.08935260 − 1.46 − 0.08893760 − 1.45 CBSAor- 0.02930090 1.15 0.03144370 1.23 Div_21780 Div_24860 CBSAor- 0.05149970 0.82 0.04866950 0.78 CBSAor- 0.00469960 0.07 0.00615690 0.09 Div_22020 Div_25060 CBSAor- − 0.21965260 − 3.30 − 0.22092540 − 3.32 CBSAor- 0.02341100 0.37 0.02504250 0.39 Div_22140 Div_25180 CBSAor- − 0.09044250 − 2.86 − 0.08956520 − 2.83 CBSAor- − 0.00325570 − 0.14 − 0.00425250 − 0.18 Div_22180 Div_25420 CBSAor- − 0.04999760 − 0.95 − 0.04962940 − 0.94 CBSAor- 0.00800100 0.10 0.00910590 0.12 Div_22220 Div_25500 CBSAor- − 0.11396160 − 2.33 − 0.11872020 − 2.43 CBSAor- − 0.02058250 − 0.87 − 0.01891210 − 0.80 Div_22280 Div_25540 CBSAor- 0.00375700 0.03 0.00456880 0.04 CBSAor- − 0.06269930 − 0.74 − 0.06172510 − 0.73 Div_22380 Div_25620 CBSAor- − 0.04576900 − 1.12 − 0.04521160 − 1.11 CBSAor- 0.18015570 2.88 0.18098160 2.89 Div_22420 Div_25860 CBSAor- − 0.07547880 − 1.16 − 0.07586360 − 1.16 CBSAor- 0.04843830 1.02 0.04993700 1.05 Div_22500 Div_25900 CBSAor- − 0.12347620 − 1.86 − 0.12200010 − 1.83 CBSAor- 0.04400040 0.93 0.04560000 0.96 Div_22520 Div_25940 CBSAor- − 1.07313000 − 7.52 − 1.07234300 − 7.51 CBSAor- − 0.19753070 − 2.68 − 0.19662010 − 2.67 Div_22660 Div_26300 CBSAor- 0.04309670 2.02 0.04382580 2.06 CBSAor- − 0.09820290 − 1.12 − 0.09708420 − 1.11 Div_22744 Div_26380 CBSAor- − 0.14298820 − 0.71 − 0.14269820 − 0.71 CBSAor- 0.04277830 2.02 0.04327510 2.04 Div_22800 Div_26420 CBSAor- − 0.04834980 − 0.69 − 0.04673860 − 0.67 CBSAor- − 0.21304570 − 2.09 − 0.21279880 − 2.09 Div_22900 Div_26580 CBSAor- − 0.09769120 − 2.48 − 0.09800110 − 2.49 CBSAor- − 0.06389590 − 1.90 − 0.06235820 − 1.85 Div_23060 Div_26620 CBSAor- 0.02338470 1.08 0.02292470 1.06 CBSAor- 0.03734950 0.44 0.04074800 0.48 Div_23104 Div_26660 CBSAor- 0.04362630 1.20 0.04701840 1.30 CBSAor- − 0.00501170 − 0.23 − 0.00716010 − 0.33 Div_23420 Div_26900 CBSAor- 0.06284370 1.76 0.06519350 1.82 CBSAor- − 0.01299940 − 0.29 − 0.01180170 − 0.27 Div_23540 Div_26980 CBSAor- − 0.01498980 − 0.25 − 0.01331430 − 0.22 CBSAor- 0.00105320 0.03 − 0.00103830 − 0.03 Div_23580 Div_27140 CBSAor- 0.05163210 1.98 0.04933540 1.89 CBSAor- − 0.03885350 − 0.68 − 0.03732500 − 0.65 Div_23844 Div_27180 CBSAor- − 0.12629830 − 0.45 − 0.12541080 − 0.44 CBSAor- − 0.00500550 − 0.22 − 0.00376920 − 0.17 Div_24020 Div_27260 The impact of Hurricanes on the value of commercial real estate 141 Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) able able CBSAor- − 0.12389810 − 1.90 − 0.12207590 − 1.88 CBSAor- 0.05458880 0.80 0.05768350 0.85 Div_27340 Div_29940 CBSAor- − 0.03316060 − 0.49 − 0.03322090 − 0.49 CBSAor- 0.01928300 0.46 0.01708390 0.41 Div_27540 Div_30140 CBSAor- − 0.02188440 − 0.64 − 0.02590390 − 0.76 CBSAor- 0.10653890 3.32 0.10533770 3.28 Div_27600 Div_30220 CBSAor- 0.03047900 0.77 0.03205990 0.81 CBSAor- − 0.10034160 − 1.44 − 0.10199680 − 1.46 Div_27620 Div_30380 CBSAor- − 0.18996690 − 1.86 − 0.18481280 − 1.81 CBSAor- − 0.01952060 − 0.67 − 0.01839540 − 0.64 Div_27740 Div_30460 CBSAor- 0.01473010 0.37 0.01913670 0.48 CBSAor- − 0.30745730 − 4.62 − 0.30630890 − 4.60 Div_27940 Div_30620 CBSAor- 0.01742060 0.54 0.01920880 0.59 CBSAor- − 0.09934910 − 3.24 − 0.09773410 − 3.19 Div_27980 Div_30780 CBSAor- 0.03386090 0.89 0.03585760 0.95 CBSAor- − 0.12101020 − 1.59 − 0.11960550 − 1.58 Div_28020 Div_30980 CBSAor- 0.00638570 0.29 0.00617970 0.28 CBSAor- 0.07912780 3.75 0.07865070 3.73 Div_28140 Div_31084 CBSAor- 0.04314310 1.35 0.04701960 1.47 CBSAor- 0.04458550 2.01 0.04178470 1.88 Div_28180 Div_31140 CBSAor- 0.07350300 1.76 0.07546730 1.80 CBSAor- − 0.16127920 − 1.13 − 0.16062280 − 1.13 Div_28420 Div_31340 CBSAor- − 0.22172320 − 3.74 − 0.21992460 − 3.71 CBSAor- − 0.15519560 − 3.36 − 0.15389440 − 3.33 Div_28540 Div_31420 CBSAor- − 0.07205850 − 0.86 − 0.07077190 − 0.84 CBSAor- 0.02559110 0.37 0.02665190 0.38 Div_28580 Div_31460 CBSAor- − 0.00999690 − 0.25 − 0.00848790 − 0.21 CBSAor- − 0.02551110 − 0.44 − 0.02343280 − 0.41 Div_28660 Div_31540 CBSAor- 0.04783380 0.76 0.04884910 0.78 CBSAor- − 0.02743750 − 1.05 − 0.02731570 − 1.05 Div_28700 Div_31700 CBSAor- − 0.05271780 − 0.57 − 0.05129700 − 0.56 CBSAor- − 0.03926970 − 0.86 − 0.03718140 − 0.82 Div_28740 Div_31740 CBSAor- 0.02708640 1.10 0.02908350 1.18 CBSAor- − 0.15683640 − 2.25 − 0.15676860 − 2.25 Div_28940 Div_31820 CBSAor- 0.10401280 2.23 0.10502040 2.25 CBSAor- − 0.01673650 − 0.27 − 0.01575960 − 0.26 Div_29100 Div_32180 CBSAor- 0.01868120 0.35 0.01690060 0.32 CBSAor- − 0.05165460 − 1.57 − 0.05359980 − 1.63 Div_29180 Div_32580 CBSAor- 0.02833120 0.81 0.02940900 0.84 CBSAor- − 0.03747760 − 0.49 − 0.03677210 − 0.48 Div_29200 Div_32780 CBSAor- 0.00109660 0.05 0.00130480 0.06 CBSAor- − 0.02190720 − 1.01 − 0.02255240 − 1.04 Div_29404 Div_32820 CBSAor- 0.06817860 1.87 0.06565440 1.80 CBSAor- − 0.25606530 − 2.79 − 0.25770190 − 2.81 Div_29420 Div_32860 CBSAor- 0.00284040 0.10 0.00354550 0.12 CBSAor- 0.06046110 2.83 0.06050180 2.83 Div_29460 Div_33124 CBSAor- 0.10624550 2.08 0.10737370 2.10 CBSAor- − 0.02754650 − 1.19 − 0.02707490 − 1.17 Div_29540 Div_33340 CBSAor- − 0.01207380 − 0.29 − 0.00990320 − 0.24 CBSAor- − 0.00195530 − 0.09 − 0.00159080 − 0.07 Div_29620 Div_33460 CBSAor- − 0.00684160 − 0.10 − 0.00546660 − 0.08 CBSAor- 0.14269180 0.71 0.14347310 0.71 Div_29740 Div_33500 CBSAor- 0.00649660 0.30 0.00695230 0.32 CBSAor- − 0.04282600 − 0.82 − 0.04176980 − 0.80 Div_29820 Div_33660 142 J. D. Fisher, S. R. Rutledge Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) able able CBSAor- 0.06599780 0.84 0.06370190 0.81 CBSAor- − 0.21518380 − 2.65 − 0.21478830 − 2.65 Div_33700 Div_36980 CBSAor- − 0.16933260 − 2.36 − 0.16615040 − 2.32 CBSAor- − 0.53151670 − 3.72 − 0.52562270 − 3.68 Div_33860 Div_37060 CBSAor- 0.00843120 0.39 0.01025840 0.47 CBSAor- 0.03864310 1.65 0.04008420 1.71 Div_33874 Div_37100 CBSAor- − 0.18511220 − 4.77 − 0.18291590 − 4.72 CBSAor- − 0.11348630 − 1.63 − 0.11240190 − 1.61 Div_34100 Div_37120 CBSAor- 0.22491730 3.60 0.22584830 3.61 CBSAor- − 0.01232990 − 0.29 − 0.01116930 − 0.26 Div_34340 Div_37140 CBSAor- 0.00598390 0.15 0.00723470 0.18 CBSAor- 0.14153230 2.08 0.14017350 2.06 Div_34820 Div_37220 CBSAor- 0.12034050 3.87 0.12212600 3.92 CBSAor- − 0.02969700 − 1.09 − 0.02812320 − 1.03 Div_34900 Div_37340 CBSAor- 0.05034720 1.99 0.05212590 2.06 CBSAor- − 0.29366090 − 2.51 − 0.29293500 − 2.50 Div_34940 Div_37460 CBSAor- 0.06662420 3.05 0.06761180 3.10 CBSAor- 0.02285230 0.37 0.02383950 0.39 Div_34980 Div_37660 CBSAor- − 0.03031980 − 1.27 − 0.02831350 − 1.19 CBSAor- 0.08406940 2.24 0.08423190 2.24 Div_35004 Div_37860 CBSAor- − 0.02038860 − 0.94 − 0.01884520 − 0.87 CBSAor- − 0.11660080 − 3.26 − 0.11566810 − 3.23 Div_35084 Div_37900 CBSAor- − 0.07603710 − 2.64 − 0.07457310 − 2.59 CBSAor- 0.01040860 0.46 0.01181320 0.53 Div_35300 Div_37964 CBSAor- − 0.02589330 − 0.94 − 0.02150100 − 0.78 CBSAor- 0.02744490 1.29 0.02833760 1.34 Div_35380 Div_38060 CBSAor- − 0.05464280 − 0.27 − 0.05515830 − 0.27 CBSAor- − 0.03190040 − 0.31 − 0.02985990 − 0.29 Div_35440 Div_38240 CBSAor- 0.05540560 2.62 0.05606180 2.65 CBSAor- − 0.02643720 − 1.15 − 0.02429790 − 1.06 Div_35614 Div_38300 CBSAor- − 0.30360780 − 1.85 − 0.30170010 − 1.84 CBSAor- 0.09858810 1.72 0.10062660 1.75 Div_35660 Div_38820 CBSAor- − 0.13614880 − 4.15 − 0.13530930 − 4.13 CBSAor- − 0.01334860 − 0.45 − 0.01174550 − 0.40 Div_35840 Div_38860 CBSAor- − 0.06334620 − 1.23 − 0.06059340 − 1.18 CBSAor- 0.06889040 3.23 0.06846580 3.21 Div_35980 Div_38900 CBSAor- 0.07295630 3.44 0.07284420 3.43 CBSAor- 0.01473850 0.56 0.01657220 0.63 Div_36084 Div_38940 CBSAor- − 0.08267180 − 1.85 − 0.08121240 − 1.81 CBSAor- − 0.00619740 − 0.27 − 0.00605800 − 0.27 Div_36100 Div_39300 CBSAor- 0.23365600 2.55 0.23478740 2.56 CBSAor- 0.09389480 1.53 0.09138870 1.49 Div_36220 Div_39340 CBSAor- 0.13148810 2.64 0.13272350 2.66 CBSAor- 0.04489030 1.35 0.04709460 1.42 Div_36260 Div_39460 CBSAor- − 0.01986660 − 0.81 − 0.02063800 − 0.84 CBSAor- − 0.01156810 − 0.30 − 0.01200960 − 0.32 Div_36420 Div_39540 CBSAor- − 0.00042240 − 0.02 0.00169160 0.08 CBSAor- 0.06639780 2.04 0.06411760 1.97 Div_39580 Div_36500 CBSAor- − 0.02593890 − 0.33 − 0.02474270 − 0.32 CBSAor- 0.00694950 0.27 0.00835070 0.32 Div_39660 Div_36540 CBSAor- 0.02341580 0.74 0.02122360 0.67 CBSAor- 0.03364750 1.57 0.03475590 1.62 Div_39740 Div_36740 CBSAor- 0.03551000 1.58 0.03544870 1.58 CBSAor- − 0.13957460 − 2.00 − 0.13741170 − 1.97 Div_39900 Div_36900 The impact of Hurricanes on the value of commercial real estate 143 Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) able able CBSAor- − 0.48142420 − 5.25 − 0.48094810 − 5.24 CBSAor- − 0.10999160 − 2.95 − 0.11161480 − 2.99 Div_39980 Div_42540 CBSAor- − 0.00960210 − 0.42 − 0.00962380 − 0.42 CBSAor- 0.08228880 3.89 0.08265010 3.91 Div_40060 Div_42644 CBSAor- 0.10623210 5.01 0.10475170 4.94 CBSAor- 0.11826030 1.51 0.12133860 1.55 Div_40140 Div_42680 CBSAor- 0.06354130 1.02 0.06073260 0.97 CBSAor- 0.21379830 3.28 0.21204220 3.26 Div_40220 Div_43100 CBSAor- 0.00437450 0.10 0.00145920 0.03 CBSAor- − 0.21161600 − 2.51 − 0.21120770 − 2.51 Div_40300 Div_43140 CBSAor- 0.01717140 0.30 0.01816500 0.32 CBSAor- 0.00902390 0.22 0.01025660 0.25 Div_40380 Div_43300 CBSAor- − 0.03750120 − 0.81 − 0.03664120 − 0.79 CBSAor- − 0.09569160 − 1.64 − 0.09671040 − 1.66 Div_40420 Div_43340 CBSAor- 0.09052400 3.39 0.09240800 3.46 CBSAor- 0.01170800 0.54 0.01398840 0.65 Div_40484 Div_43524 CBSAor- − 0.00975770 − 0.45 − 0.00825920 − 0.38 CBSAor- 0.04961740 0.63 0.05082830 0.65 Div_40900 Div_43580 CBSAor- 0.01050490 0.16 0.01076980 0.17 CBSAor- 0.05420990 0.90 0.05518720 0.92 Div_41060 Div_43620 CBSAor- 0.00504310 0.23 0.00521270 0.24 CBSAor- − 0.09273170 − 2.32 − 0.09252370 − 2.32 Div_41180 Div_43780 CBSAor- 0.02857930 0.20 0.02940910 0.21 CBSAor- 0.05813520 1.18 0.05538620 1.12 Div_41420 Div_43900 CBSAor- 0.05863430 1.46 0.06020630 1.50 CBSAor- 0.18025260 3.90 0.17755230 3.84 Div_41460 Div_44060 CBSAor- 0.01377610 0.41 0.01530860 0.46 CBSAor- − 0.21345020 − 4.91 − 0.21152660 − 4.86 Div_41500 Div_44140 CBSAor- − 0.13790690 − 2.02 − 0.13519050 − 1.98 CBSAor- 0.23211850 2.96 0.23327410 2.97 Div_41540 Div_44180 CBSAor- 0.03160430 1.41 0.03267340 1.46 CBSAor- − 0.04012310 − 0.49 − 0.03696890 − 0.46 Div_41620 Div_44260 CBSAor- 0.01377140 0.63 0.01416410 0.65 CBSAor- − 0.52972930 − 4.14 − 0.52914840 − 4.13 Div_41700 Div_44340 CBSAor- 0.05250510 2.47 0.05335130 2.51 CBSAor- − 0.09630910 − 1.80 − 0.09502500 − 1.78 Div_41740 Div_44420 CBSAor- 0.09862270 4.61 0.10050470 4.70 CBSAor- 0.07183550 2.95 0.06971310 2.87 Div_41884 Div_44700 CBSAor- 0.05760110 2.70 0.05777720 2.71 CBSAor- − 0.14841490 − 1.89 − 0.15292740 − 1.95 Div_41940 Div_45060 CBSAor- 0.03042880 1.00 0.03198710 1.05 CBSAor- 0.12466630 5.53 0.12220060 5.42 Div_42020 Div_45104 CBSAor- 0.08786340 3.74 0.09058530 3.85 CBSAor- 0.02607360 0.85 0.02820450 0.92 Div_42034 Div_45220 CBSAor- 0.02538300 0.62 0.02660410 0.65 CBSAor- 0.02279310 1.06 0.02464630 1.14 Div_42100 Div_45300 CBSAor- 0.03980890 1.39 0.04228720 1.48 CBSAor- − 0.10874780 − 1.39 − 0.10776200 − 1.38 Div_42140 Div_45460 CBSAor- 0.03147910 1.16 0.03228720 1.19 CBSAor- − 0.06602140 − 1.03 − 0.06474480 − 1.02 Div_42200 Div_45500 CBSAor- 0.08286040 3.16 0.08476810 3.23 CBSAor- 0.82549570 12.67 0.82661550 12.69 Div_42220 Div_45520 CBSAor- − 0.04732210 − 1.11 − 0.04610870 − 1.08 CBSAor- 0.09185280 1.13 0.09509510 1.17 Div_42340 Div_45540 144 J. D. Fisher, S. R. Rutledge Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) able able CBSAor- − 0.00891690 − 0.24 − 0.00777810 − 0.21 CBSAor- − 0.38295830 − 4.17 − 0.38173280 − 4.16 Div_45780 Div_48940 CBSAor- − 0.01729470 − 0.37 − 0.01296280 − 0.28 CBSAor- 0.06046340 1.04 0.06273630 1.08 Div_45820 Div_49020 CBSAor- 0.07847140 0.67 0.07881730 0.67 CBSAor- − 0.09769220 − 2.13 − 0.09654470 − 2.11 Div_45860 Div_49180 CBSAor- 0.06496440 2.68 0.06584670 2.72 CBSAor- − 0.02457440 − 1.03 − 0.02524680 − 1.06 Div_45940 Div_49340 CBSAor- 0.08125420 2.32 0.08287790 2.36 CBSAor- − 0.03406850 − 0.89 − 0.03230780 − 0.84 Div_46020 Div_49420 CBSAor- − 0.02807500 − 1.06 − 0.02755890 − 1.04 CBSAor- − 0.08854540 − 2.06 − 0.08782240 − 2.05 Div_46060 Div_49620 CBSAor- − 0.00146830 − 0.06 − 0.00080000 − 0.03 CBSAor- 0.00583120 0.09 0.00682860 0.11 Div_46140 Div_49700 CBSAor- − 0.08106300 − 0.92 − 0.07972340 − 0.91 CBSAor- 0.13135090 1.73 0.13219290 1.74 Div_46180 Div_49740 CBSAor- − 0.00033170 − 0.01 0.00064310 0.01 CBSAor- − 0.10569670 − 1.87 − 0.10385900 − 1.84 Div_46220 Div_49780 CBSAor- 0.07008260 1.16 0.07127200 1.18 Div_46300 CBSAor- − 0.54376900 − 5.93 − 0.54250340 − 5.91 Div_46500 Open Access This article is licensed under a Creative Commons Attri- CBSAor- − 0.02806810 − 1.04 − 0.02612630 − 0.97 bution 4.0 International License, which permits use, sharing, adapta- Div_46520 tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, CBSAor- 0.12283970 1.96 0.12013270 1.92 provide a link to the Creative Commons licence, and indicate if changes Div_46540 were made. The images or other third party material in this article are CBSAor- − 0.15036310 − 1.98 − 0.14980330 − 1.97 included in the article’s Creative Commons licence, unless indicated Div_46660 otherwise in a credit line to the material. If material is not included in CBSAor- 0.04154590 1.63 0.04094140 1.61 the article’s Creative Commons licence and your intended use is not Div_46700 permitted by statutory regulation or exceeds the permitted use, you will CBSAor- − 0.06045500 − 1.30 − 0.05847260 − 1.25 need to obtain permission directly from the copyright holder. To view a Div_46740 copy of this licence, visit http://cr eativ ecommons. or g/licen ses/ b y/4.0/ . CBSAor- 0.00116330 0.05 0.00308160 0.13 Div_47260 CBSAor- 0.37252660 5.95 0.37317330 5.97 Div_47300 References CBSAor- − 0.04284580 − 1.91 − 0.04112840 − 1.83 Div_47664 Bleich, Donald. 2003. The Reaction of Multifamily Capitalization CBSAor- 0.03246550 1.54 0.03463630 1.64 Rates to Natural Disasters. Journal of Real Estate Research 25 Div_47894 (2): 133–144. Graham, Jr., J. Edward, and William W. Hall, Jr. 2001. Hurricanes, CBSAor- − 0.04156440 − 0.95 − 0.04086620 − 0.93 Housing Market Activity, and Coastal Real Estate Values. The Div_47940 Appraisal Journal 69 (4): 379–387. CBSAor- 0.03875110 1.80 0.04052880 1.88 Graham, Edward, William Hall,  and Peter Schuhmann. 2007. Hur- Div_48424 ricanes, Catastrophic Risk, and Real Estate Market Recovery. CBSAor- − 0.03747680 − 0.70 − 0.03823650 − 0.72 Journal of Real Estate Portfolio Management 13 (3): 179–190. Div_48620 Morgan, Ash. 2007. The Impact of Hurricane Ivan on Expected Flood CBSAor- 0.05614910 1.22 0.05692420 1.24 Losses, Perceived Flood Risk, and Property Values. Journal of Div_48660 Housing Research 16 (1): 47–60. CBSAor- − 0.49116150 − 6.85 − 0.49072090 − 6.85 Salter, Sean P., and Ernest W. King. 2009. Price Adjustment and Div_48780 Liquidity in a Residential Real Estate Market with an Acceler- ated Information Cascade. Journal of Real Estate Research 31 CBSAor- − 0.01527400 − 0.64 − 0.01637680 − 0.68 (4): 421–454. Div_48864 Simmons, Kevin M., Jamie Brown Kruse, and Douglas A. Smith. 2002. CBSAor- 0.01675230 0.39 0.01937340 0.45 Valuing Mitigation: Real Estate Market Responses to Hurricane Div_48900 The impact of Hurricanes on the value of commercial real estate 145 Loss Reduction Measures. Southern Economic Journal 68 (3): Sara R. Rutledge Ms. Rutledge is the Founder and Principal Econo- 660–671. mist of SRR Consulting. She provides expert research and analysis on macroeconomic and real estate topics for a variety of public and private sector clients. Her past and current consulting projects include invest- Publisher’s Note Springer Nature remains neutral with regard to ment strategy white papers, real estate market research and analysis, and jurisdictional claims in published maps and institutional affiliations. report content creation and management. Ms. Rutledge was previously the Managing Director of Real Estate Products at StratoDem Analytics, an early-stage data science firm delivering market intelligence tools to the real estate industry. She applied her real estate experience in Jeffrey D. Fisher Ph.D. is a Professor Emeritus of Real Estate at the this role to improve and develop UI/UX for to ensure the platform met Indiana University Kelley School of Business, and a Visiting Profes- the research needs of the industry and support clients with incorpo- sor at Johns Hopkins University. He is the Research and Education ration of the platform into their existing research processes. She has Consultant to the National Council of Real Estate Investment Fidu- also served as the Director of Research for the National Council of ciaries (NCREIF) and President of the Homer Hoyt Institute. He is Real Estate Investment Fiduciaries (NCREIF), managing all research a member of the advisory committee to the Real Estate Finance and activities for the private real estate investment management industry Economics Institute at Ecole hôtelière de Lausanne in Switzerland. association. This work included industry education on quarterly data Professor Fisher is a coauthor of Real Estate, 9th edition published product releases via a live webinar presentation, in-house reporting, and by John Wiley and Sons, coauthor of Real Estate Finance and Invest- published articles for Institutional Real Estate Investor. Other previous ments, 14th edition, published by McGraw-Hill, and coauthor of roles include serving as CBRE’s Director of Research and Analysis Income Property Valuation, published by Dearborn. His books have for Texas, and eight years in North American investment research at been translated into Japanese, Korean, and Chinese. Dr. Fisher has Invesco Real Estate. Ms. Rutledge has also taught research methods for published numerous articles in journals such as The Journal of the the Institute of Applied Economics at the University of North Texas. American Real Estate and Urban Economics Association, Journal Ms. Rutledge serves on the ULI Chicago Women’s Leadership Initia- of Real Estate Finance and Economics, The Journal of Urban Eco- tive (WLI) Advisory Board and is active in the ULI Research Forum nomics, The Journal of Real Estate Research, Journal of Portfolio and national WLI initiatives. She previously served on the Real Estate Management, National Tax Journal, Public Finance Quarterly, The Research Institute Advisory Board and National Association for Busi- Appraisal Journal, Real Estate Review, The Real Estate Appraiser and Analyst, Real Estate Issues, The New Corporate Finance, and the ness Economics (NABE) Board of Directors. For which she remains Journal of Property Tax Management. Education: Ph.D., Real Estate, an active member and committee volunteer. Education: M.S., Applied Ohio State University; MBA, Wright State University; B.S., Industrial Economics, University of North Texas; BBA, minor study in Economics Management, Purdue University. and Mathematics, University of North Texas. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Business Economics Springer Journals

The impact of Hurricanes on the value of commercial real estate

Business Economics , Volume 56 (3) – Mar 22, 2021

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Publisher
Springer Journals
Copyright
Copyright © The Author(s) 2021
ISSN
0007-666X
eISSN
1554-432X
DOI
10.1057/s11369-021-00212-9
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Abstract

Commercial real estate investors prefer coastal, gateway, markets for liquidity, demand density, and durable returns. Yet, these areas are more vulnerable to the effects of climate change from more intense and frequent weather events such as hurricanes and typhoons as well as to gradual changes such as sea-level rise. Recognition is growing of the risks that these events pose to investment performance, but little is known about how this risk has impacted property values and returns when an event such as a hurricane occurs. This is the first study to analyze the impact on property values and returns from hurricanes causing the most significant damage by value over the past 30-plus years throughout the nation. Using individual property data from the National Council of Real Estate Investment Fiduciaries database, we find a significant impact on the value and rates of return, after accounting for any additional capital expenditures for repairs, for properties that are in areas impacted by a hurricane, relative to areas that were not impacted by a hurricane. These impacts vary by property type and can last for several years after the hurricane hit land in the area. Keywords Real estate · Investment · Property risk · Hurricane · Climate change 1 Introduction to National Oceanic and Atmospheric Administration estimates. Investor preferences for coastal, gateway, markets mean that For leading real estate investors and investment manag- many assets held by real estate investors are in cities more ers, the need to understand and develop strategies to address vulnerable to the effects of climate change. These effects climate-related risks needs to be understood and prioritized. range from more intense and frequent weather events such Yet little is known about how this risk has impacted prop- as hurricanes and typhoons to gradual changes such as sea- erty values and returns when an event such as a hurricane level rise. Recognition is growing of the risks that these occurs. The impact extends beyond the direct damage to the events pose to investment performance. Recent weather property. An increase in the perceived risk of hurricanes events caused significant physical damages to properties can result in a reluctance by institutional investors to add and infrastructure. In 2017, the year Hurricanes Harvey and capital to that area, which can lead to a negative impact on Maria hit the United States and storms battered northern property values and returns well after the hurricane to all and central Europe, insurers paid out a record $135 billion properties and property types in the area that was impacted globally for damage caused by storms and natural disasters. by a hurricane. This is the first study to analyze the impact This figure does not represent actual damages, which in the on property values and returns from all the significant hur - United States alone which equaled $307 billion, according ricanes that occurred over the past 30-plus years throughout the nation. We find that there has been a significant impact on the value and rates of return, after accounting for any * Sara R. Rutledge additional capital expenditures for repairs, for properties that srrutledge@gmail.com are in areas impacted by a hurricane relative to areas that Jeffrey D. Fisher were not impacted by a hurricane. This can last for several jfishe48@jhu.edu years after the hurricane hit land in the area. Homer Hoyt Institute, 760 US Highway 1, Suite 300, North Palm Beach, FL 33408, USA SRR Consulting, 1620 Fowler Avenue, Evanston, IL 60201, USA Vol.:(0123456789) 130 J. D. Fisher, S. R. Rutledge largely related to recurring hurricane landfalls and increased 2 Literature review perceptions of catastrophic risk. Morgan (2007) also address the topic of risk perceptions The real estate literature is limited on this topic. There is before after Hurricane Ivan. The author’s focus was on research exploring the impact of natural disasters on resi- how subsidized insurance premiums could create a market dential property market activity and valuation. This research imbalance by reducing expected flood losses and perceived largely covers single-family housing with limited coverage risks associated with living in floodplain areas. In addition on multifamily properties. Although other commercial prop- to the findings on Santa Rosa County, Florida home prices erty types are rarely considered, these studies provide three declines after the hurricane, the author observed that dam- common themes: (1) natural disasters negatively impact real age from Ivan led to significant changes in flood insurance estate, (2) increased expectations of natural disasters amplify premiums. The research estimated pre- and post-hurricane this negative impact, and (3) negative real estate impacts capitalized values of flood insurance premiums to conclude fade over time. that expected flood losses in the county rose by 75% after The literature, as in Graham and Hall (2001), Bliech Ivan, amplifying the perceived risk of properties located in (2003), and Morgan (2007), establishes that natural disasters these floodplains. have a negative impact on real estate pricing and/or values. The importance to this study lies in the increasing fre- Graham and Hall (2001), in a study of multiple hurricanes quency of large and powerful hurricanes due to the chang- making landfall in the Wilmington area of North Carolina, ing global climate. Highlighting this shift in frequency and find that residential property values face increasingly nega- intensity is the fact that 14 of the top 20 costliest mainland tive impacts from successive hurricanes. Also relevant to United States tropical cyclones have occurred from 2000 to this paper is that Graham and Hall (2001) used factors in 2017, two of which made landfall in 2017—Harvey (tied their model to control for other impacts on housing values, with Katrina 2005 for costliest storm) and Irma (ranked such as location and economic conditions. fifth). Morgan (2007) researched Hurricane Ivan’s 2004 impacts Graham et al. (2007) expanded upon Graham and Hall’s on risk perceptions and housing values in Santa Rosa earlier research to study the duration of negative impacts County, Florida. The author identifies reduced home sale to housing values following landfall of a hurricane. The prices for properties in high-risk flood areas after the hur - authors’ findings support the same pattern of increasing ricane. Housing in these floodplains commanded a premium home price declines with each successive hurricane. Then, relative to other houses in the county prior to the hurricane, their research reveals a tempering of this trend with a price as federally insured areas in desirable waterfront locations. recovery and the return to pre-storm market stability over the It is noteworthy that the observed 15% post-Ivan reduc- 3 years following the last major hurricane strike. tion in prices did not eliminate the home price premium in Similarly, Salter and King (2009) found that an unan- floodplains. nounced event can cause an overreaction that the market Turning to a  different type of natural disaster, Bleich will correct via pricing adjustments, although informa- (2003) researched the impact of the 1994 Northridge Earth- tion inefficiencies can create a lag in this correction. Their quake on the Los Angeles multifamily property market. A research covered the post-Hurricane Katrina housing market negative impact on values was identified using capitalization in Hattiesburg, Mississippi—an area on the periphery of the rates, which rose overall. However, multifamily capitaliza- storm’s impact. This study of a market less damaged, but tion rates increased the most near the earthquake’s epicenter adjacent to more devastated areas, yields additional infor- and areas not experiencing damage did not have valuation mation about the complexity of hurricane impacts on real impacts. estate pricing and liquidity. The supply/demand balance for The Graham and Hall (2001) findings of increasingly neg- housing tightened due an increase in housing demand from ative impacts on housing values led the authors to conclude people displaced by the storm and a reduction, at least tem- that a period of unprecedented hurricane activity increased porarily, in supply from storm damage. Thus, the market the market expectations of catastrophic risk, contributing correction is related to the timing for bringing supply back to market instability. Housing in the Wilmington area of online through repairs or new construction. North Carolina was studied after landfall by hurricanes Ber- In the Bleich (2003) earthquake study, proximity to the tha and Fran in 1996, Bonnie 1998, and Floyd 1999. Results natural disaster was found to be a factor in the real estate revealed limited effects after the 1996 storms, but succes- sively more extreme and immediate negative consequences on real estate values were observed after Bonnie and Fran. This structural shift in the housing market was identified as National Hurricane Center, Costliest U.S. tropical cyclone tables updated, Table  3a. https:// www. nhc. noaa. gov/ news/ Updat edCos tliest. pdf. The impact of Hurricanes on the value of commercial real estate 131 effects with first year impacts correlated to the epicenter and were not impacted either during the same time as a hurri- areas with the high concentrations of damage. The author cane, or ever impacted by a hurricane, to capture the relative also found these impacts to be temporary because, by the impact of hurricanes on property values and returns. third year of the analysis, negative effects were not signifi- Nineteen hurricanes making U.S. landfall were included, cant. There were, however, lingering effects on values for as summarized in Table 1. The table lists the hurricane’s older multifamily buildings with architectural styles that name, landfall date, estimated damage, and impacted loca- proved to be less resistant to earthquakes. On the flipside, tions. The NHC’s most recent list of the costliest U.S. tropi- Simmons et al. (2002) find that risk mitigation factors in a cal cyclones (updated 2018) was used to identify a list of Gulf Coast city to protect homes from hurricane damage major hurricanes. The NHC damage estimates include enhanced home values. insured and uninsured losses and are estimated using This paper addresses all three themes by researching the source data from Federal Emergency Management Agency, impact on real estate values from the most catastrophic hur- U.S. Department of Agriculture, National Interagency Fire ricanes over the years after these storms. Most importantly, Center, U.S. Army Corps of Engineers, state emergency this paper fills a significant gap in the literature by examin- management agencies, state and regional climate centers, ing these impacts on commercial real estate values. and insurance industry estimates. This broad assessment of damages reveals a list of storms most likely to affect com- mercial real estate. The fifteenth costliest hurricane on the 3 Data NHC list (Allison) made landfall as a tropical storm. Given its ranking on the list and impact to a major real estate mar- The data for this study come from the National Council of ket (Houston), this storm was included in the analysis. Real Estate Investment Fiduciaries (NCREIF). NCREIF is The Census-defined core-based statistical areas (CBSAs) a non-profit, membership organization of the institutional and divisions impacted by these hurricanes were determined investment managers that invest in U.S. commercial real by reviewing detailed cyclone reports from the U.S. Depart- estate on behalf of their clients, including high net worth ment of Commerce National Oceanic and Atmospheric individuals, pension funds, and endowments. NCREIF was Administration National Hurricane Center (NHC). CBSAs formed to create benchmarks to track the performance of are U.S. geographic areas defined by the Office of Manage- commercial real estate as an asset class. ment and Budget, and consist of one or more counties (or The NCREIF Property Index (NPI) begins in 1978 and equivalents) anchored by an urban center of at least 10,000 includes quarterly data on five major property types: apart- people plus adjacent counties that are socioeconomically ment, hotel, industrial, office, and retail. The quarterly prop- tied to the urban center by commuting. Larger CBSAs by erty data are provided directly to NCREIF from the account- population may have multiple divisions within them. If a ing of individual property performance by their investment CBSA has a division, we use the division instead of the management membership. Data on income and capital entire CBSA. expenditures are provided on the properties each quarter in The CBSAs and divisions in this analysis were selected addition to appraised property values, because managers use based upon each hurricane’s tracked path from landfall until current value accounting for performance reporting to inves- the storm was no longer categorized as a hurricane per NHC tors. Investment manager members also report data on other reporting. In some cases, a CBSA or division identified as property types, such as self-storage and senior housing, and being impacted by a hurricane did not have property data these data are included in the complete property database in the NCREIF database and had to be excluded from this available for research use. As of fourth quarter 2019, the NPI analysis. includes 8262 properties with an aggregate market value of $658.4 billion, and the complete property database includes 10,213 properties valued at $741.2 billion. The historical 4 Methodology database has information on nearly 800,000 properties, including those that have been sold over time. This study is designed to capture the impact of a hurricane For this study, individual property data were used from on all the properties in CBSAs and/or divisions. We measure 1989 to 2019, which spans the period the major hurricanes the impact of a hurricane on a CBSA or division that had a occurred that were included in this study. Property types included office, retail, apartment, industrial, and hotel, span- ning the entire U.S. There were over 400,000 property obser- Locations impacted by hurricanes without corresponding NCREIF vations (cross sectional and time series) depending on the data include: Dover, DE (Hurricane Isabel), Lafayette, LA (Hurricane panel regression used, as discussed below. The data cover Gustav), Lake Charles, LA (Hurricanes Harvey and Rita), and More- both areas that were impacted by hurricanes and areas that head City, NC (Hurricanes Irene and Floyd). 132 J. D. Fisher, S. R. Rutledge Table 1 Major U.S. Hurricanes Hurricane name U.S. Landfall quarter NHC damage est. Impacted CBSAs and divisions (billions, nominal) Allison 2Q 2001 $8.5 Houston-The Woodlands, TX Andrew 3Q 1992 $27.0 Baton Rouge, LA New Orleans, LA Fort Lauderdale, FL West Palm Beach, FL Miami, FL Charley 3Q 2004 $16.0 Daytona Beach, FL Orlando, FL Fort Meyers, FL Tampa, FL Myrtle Beach, SC Floyd 3Q 1999 $6.5 Virginia Beach-Norfolk, VA Wilmington, NC Fran 3Q 1996 $5.0 Myrtle Beach, SC Washington, DC Raleigh-Durham, NC Wilmington, DE Frances 3Q 2004 $9.8 Daytona Beach, FL Tampa, FL Orlando, FL West Palm Beach, FL Port St. Lucie, FL Gustav 3Q 2008 $6.0 Baton Rouge, LA New Orleans, LA Harvey 3Q 2017 $125.0 Houston-The Woodlands, TX Corpus Christie, TX Beaumont-Port Arthur, TX Victoria-Port Lavaca, TX Hugo 3Q 1989 $7.0 Charleston, SC Columbia, SC Charlotte, NC Myrtle Beach, SC Ike 3Q 2008 $30.0 Houston-The Woodlands, TX Beaumont-Port Arthur, TX Irene 3Q 2011 $13.5 Atlantic City, NJ New York, NY Jacksonville, NC Virginia Beach-Nor- Nassau Co-Suffolk Co, NY folk, VA Irma 3Q 2017 $50.0 Fort Lauderdale, FL Orlando, FL Fort Meyers, FL Port St. Lucie, FL Gainesville, FL Savannah, GA Miami, FL Tampa, FL Naples, FL West Palm Beach, FL Isabel 3Q 2003 $5.5 Baltimore, MD Washington, DC Virginia Beach-Norfolk, VA Wilmington, DE Jeanne 3Q 2004 $7.5 Daytona Beach, FL Tampa, FL Orlando, FL West Palm Beach, FL Port St. Lucie, FL Katrina 3Q 2005 $125.0 New Orleans, LA Gulfport, MS Matthew 4Q 2016 $10.0 Charleston, SC Myrtle Beach, SC Savannah, GA Hilton Head, SC Wilmington, NC Jacksonville, FL Jacksonville, NC Rita 3Q 2005 $18.5 Houston-The Woodlands, TX Beaumont-Port Arthur, TX Sandy 4Q 2012 $65.0 Atlantic City, NJ New York, NY Camden, NJ Newark, NJ Nassau Co-Suffolk Co, NY Ocean City, NJ Wilma 4Q 2005 $19.0 Fort Lauderdale, FL Miami, FL Fort Meyers, FL West Palm Beach, FL hurricane whether it was physically damaged or not. Insti- values after a hurricane—especially if the risk of future hur- tutional investors can choose to allocate less or no capital ricanes is perceived to have increased due to climate change. to areas that have been impacted by a hurricane and tenants Panel regression using cross-sectional and time-series can be more reluctant to sign leases in those areas. All these data methodology were estimated. For every property and factors can impact occupancy, risk premiums and property for every quarter, we calculated the cumulative change in The impact of Hurricanes on the value of commercial real estate 133 Table 2 Variable summary Variable Type Description Property value Dependent Quarterly appraisal-based property market value per NCREIF Property total return Dependent Quarterly property investment return from income and appreciation per NCREIF Property capital return Dependent Quarterly property return from market appreciation per NCREIF HurricaneQtr Dummy Indicator is 1 for properties in a location (CBSA or Division) impacted by a major hurricane in during the quarter CBSAorDiv Dummy Dummy variables for all locations (CBSA or Division) to control for fixed effects yyyyq Dummy Dummy variables for each quarterly period to control for property market conditions over time Square feet (sqft) Independent Property size in square feet per NCREIF Sqft2 Independent Squared property size to allow for nonlinear relationship to property performance Age Independent Property age in quarters from completion date per NCREIF Age2 Independent Squared property age in quarters to allow for nonlinear relationship to property performance Percentleased Independent Leased square feet in a property as a share of the property’s total square feet for the quarter before the hurricane Apartmenthq Interaction Interaction dummy variable with an indicator of 1 for apartment properties in a hurricane quarter Industrialhq Interaction Interaction dummy variable with an indicator of 1 for industrial properties in a hurricane quarter Officehq Interaction Interaction dummy variable with an indicator of 1 for office properties in a hurricane quarter Retailhq Interaction Interaction dummy variable with an indicator of 1 for retail properties in a hurricane quarter value and cumulative return over the next 1, 2, 3, 4, and To control for the fixed effects of different locations in 5-year periods. This process creates the dependent variables the U.S., dummy variables were also created for each CBSA that are used in the various models. For example, one model or division regardless of whether it was impacted by a hur- will examine how the value changed over the four quarters ricane. These are strictly cross-sectional dummy variables. after the hurricane landfall quarter. This change in value Similarly, we created dummy variables for each quarter will be calculated for all properties whether they were in the to control for changes in market conditions over time. The CBSA or division impacted by the hurricane or not so we coefficients of these quarterly dummy variables could be can compare the relative change in value. used to create a national price index. As a check on the Similar dependent variables were created for the capital validity of the model, we verified that this price index return (or appreciation), which is a measure of the change essentially replicated the equal weighted version of the in value net of capital expenditures (capex) and for the total NCREIF Property Index. return, which is the combination of income and capital returns. Appreciation by itself was used in addition to the change in value because properties impacted by hurricanes might have incurred more capex for repairs than properties Table 3 Regression results for cumulative property value change, 8 quarters after hurricane not impacted by a hurricane. This allows us to consider that some of the loss in value from the hurricane may have been Variable Coefficient Stnd. error t-stat restored by additional capex being spent on the property. Constant 0.12153350 0.07604170 1.60 To determine whether a hurricane impacted the value Sqft 0.00000001 0.00000000 9.07 change and other measures discussed above after the hurri- Sqft2 0.00000000 0.00000000 − 6.09 cane, a dummy variable was used to indicate if the property Age 0.00034200 0.00003520 9.72 is in the CBSA or division impacted by one of the hurricanes Age2 − 0.00000015 0.00000002 − 8.47 during the quarter of the hurricane. The dummy variable Percentleased 0.05253790 0.00391590 13.42 is 1 if the property is in the CBSA or division where there HurricaneQtr − 0.25885300 0.07441150 − 3.48 was a hurricane during that quarter. Otherwise, the dummy Observations 334,132 variable was zero. Thus, the coefficient of this variable indi - MSE 0.09697279 cates the marginal impact of a property being in the area F test (probability) 0.00000000 of the hurricane in the quarters after the occurrence of the hurricane. 134 J. D. Fisher, S. R. Rutledge 5% Figure 1 Cumulative property 0.4% value change for quarters after 0% hurricanes -5% -10% -14.0% -15% -22.0% -20% -25.0% -25% -30% -35% -40% -45.0% -45% -50% 4Q 8Q 12Q16Q 20Q variable indicates how each property type was impacted Table 4 Regression results for cumulative property value change with property-type interaction terms, 8 quarters after hurricane relative to the impact on hotels. The variables described in this section are summarized Variable Coefficient Stnd. error t-stat in Table 2 above. Regressions were run separately for each Constant 0.12376260 0.07602000 1.63 of the time periods after the hurricane (1 year after, 2 years Sqft 0.00000001 0.00000000 8.93 after, etc.) and for each of the different dependent variables Sqft2 0.00000000 0.00000000 − 6.05 (price change, capital return, and total return). The results Age 0.00035140 0.00003520 9.97 are discussed in the next section. Age, squared − 0.00000015 0.00000002 − 8.79 Percent leased 0.04967120 0.00393010 12.64 HurricaneQtr − 0.30781210 0.07508430 − 4.10 5 Results Apartmenthq 0.04246640 0.01064470 3.99 Industrialhq 0.07234580 0.01050950 6.88 Table 3 shows the results of one of the regressions of the Officehq 0.03552060 0.01061390 3.35 impact of hurricanes on the cumulative change in value eight Retailhq 0.04477270 0.01074850 4.17 quarters after the hurricane. There were 334,132 property- Observations 334,132 quarter observations. The cofficients on the dummy vari - MSE 0.09697279 ables used for each location (CBSA or Division) and quarter F test (probability) 0.00000000 are not shown below, but are provided in the Appendix. The estimated coefficients on the variables for square feet, square feet squared, age, and age squared are all highly Independent variables were also used to control for the significant, as is the occupancy at the time of the hurricane. fact that the change in value, and returns for a property The hurricane dummy variable indicates that the property tend to vary with the size (measured in square feet) and was in the CBSA or division impacted by a hurricane on age (measured in quarters from property completion date) the quarter the hurricane made U.S. landfall. It indicates of the property. These impacts tend to be nonlinear. Thus, how much this affected the cumulative change in value over we included variables for the property square footage, the following eight quarters relative to how the properties square footage squared, property age, and age squared. If performed that were not in the areas impacted by the hur- the relationship turned out to be linear, the coefficients of ricane. In this model, the property-type interaction vari- these squared variables would be insignificant. We also ables are omitted so we can get an indication of the overall included the occupancy of each property as of the quarter impact on a portfolio of all property types. The results sug- prior to the hurricane as an independent variable. gest that over the eight quarters following the hurricane Finally, we created interaction dummy variables to quarter, property values increased by 25.9% less than prop- indicate what the property type is for a property that was erties not impacted by a hurricane, or 3.2% per quarter. impacted by a hurricane. For example, the office interac- The same regression was run for 1, 3, 4, and 5-year tion dummy variable was 1 if it was an office property time periods following hurricane landfall for all prop- with the hurricane dummy of 1 in the area of a hurricane erty types combined. Figure 1 is a graph of the impact on as of the quarter of a hurricane. Hotel properties were the value change over time. We see that the impact on value omitted dummy variable. The coefficient of this dummy The impact of Hurricanes on the value of commercial real estate 135 Table 5 Cumulative value change by property type, from 1 to 5 years Table 6 Cumulative appreciation change by property type, from 1 to after hurricane 5 years after hurricane Property type Quarters after Hurricane Property type Quarters after Hurricane 4Q (%) 8Q (%) 12Q (%) 16Q (%) 20Q (%) 4Q (%) 8Q (%) 12Q (%) 16Q (%) 20Q (%) Hotel − 14.5 − 30.0 − 41.0 − 21.0 − 2.0 Hotel − 11.0 − 27.3 − 43.3 − 24.0 − 7.4 Apartment − 13.8 − 26.0 − 41.7 − 19.0 5.0 Apartment − 7.1 − 24.9 − 39.8 − 14.2 6.2 Industrial − 12.4 − 23.0 − 46.0 − 23.0 − 1.6 Industrial − 8.4 − 24.9 − 46.2 − 20.4 − 3.4 Office − 15.5 − 26.5 − 45.0 − 18.0 − 3.5 Office − 9.9 − 27.8 − 47.3 − 25.1 − 7.4 Retail − 13.0 − 25.5 − 42.0 − 19.0 5.0 Retail − 8.7 − 27.4 − 41.9 − 16.3 5.5 Figure 2 Cumulative value 10% change for quarters after hur- ricanes by property type 0% -10% -20% -30% -40% -50% 4Q 8Q 12Q16Q 20Q Office Retail Apartment Industrial Hotel continues to be negative until 3 years (or 12 quarters) the total impact is still negative when the coefficient for the after the hurricane, and then, the impact starts to dissi- interaction dummy is added to the coefficient of the hurricane pate with values recovering 5 years (or 20 quarters) after quarter dummy. Table 5 shows the results when regressions are the hurricane. run for 1 to 5 years, and Fig. 2 graphs the results. Table 4 shows the regression results when the property- We have shown the impact of hurricanes on the cumula- type interaction dummies are added. For example, the apart- tive change in value after the ocurrance of a hurricane on ment dummy is 1 if there is a hurricane during the quarter each property type. This is the impact on all properties in the in that CBSA or division and the property is an apartment. area of the hurricane regardless of whether they were actu- The impact of a hurricane is then found by adding the coef- ally physically damaged. Those properties that were physi- ficient from the hurricane quarter dummy to the interaction cally damaged may have had repairs after the hurricane that dummy for each property type. Since hotels are the omitted would restore the loss in value due to the damage—but not property-type interaction variable, the impact on hotels is any impact due to less demand by tenants and investors for just the coefficient of the hurricane quarter dummy. properties in the hurricane impacted area. The hurricane quarter dummy variable (as well as each of To control for the capital expenditure on repairs, we cal- the property-type interaction variables) is statistically signifi- culated the cumulative capital return for each property for 1, cant. The positive coefficients for the property-type dummies 2, 3, 4, and 5 years after the hurricane. The capital return is indicate that the impact of the hurricane is less for that property the change in value net of capital expenditures. That is, if the type than for the omitted hotel interaction variable. However, value increased only because of capital expenditures, the capi- tal return would be zero. The regressions discussed above were repeated using the cumulative capital return as the dependent An alternative approach is to leave out the hurricane quarter variable. The results are shown in Table 6 and Fig. 3. dummy when including the property-type interaction variables. In this case, the property-type interaction variables would capture the Finally, we examined the impact of hurricanes on the total full impact of the hurricane on that property type. There would be return that investors would receive from income and capital no need to leave out a property type because the “omitted variable” appreciation net of capex. In this case, the coefficients indi- would be when there is no hurricane that quarter in a CBSA or divi- cate how much the return is impacted relative to the NCREIF sion. Using this approach resulted in essentially the same results for the impact of the hurricane on each property type. Property Index (NPI) in areas not impacted by a hurricane. 136 J. D. Fisher, S. R. Rutledge Figure 3 Cumulative apprecia- 10% tion change for quarters after 0% hurricanes by property type -10% -20% -30% -40% -50% 4Q 8Q 12Q16Q 20Q Office Retail Apartment Industrial Hotel Table 7 Cumulative change in total return by property type, from 1 to since 1988, which include 19 storms that impacted differ - 5 years after hurricane ent areas of the U.S. After controlling for property size, age, location, time (market conditions), and occupancy, Property type Quarters after Hurricane we find that hurricanes appear to have a significant impact 4Q (%) 8Q (%) 12Q (%) 16Q (%) 20Q (%) on property values, appreciation (net of capex), and total Hotel − 5.0 − 24.5 − 43.8 − 7.0 5.3 return. Apartment − 6.1 − 31.5 − 52.2 − 11.0 4.3 The impact on all three measures peaked 3 years after Industrial − 5.9 − 28.5 − 54.2 − 12.0 − 0.7 hurricane landfall and then began to dissipate over the fol- Office − 7.4 − 32.0 − 56.8 − 19.4 − 6.7 lowing 2 years. Five years after a major hurricane, apartment Retail − 6.7 − 32.5 − 51.7 − 9.4 7.3 and retail properties had recovered, but office, hotel, and industrial still experienced a cumulative negative impact on the capital return. The results of this study are important for investors decid- The cumulative return was calculated from 1 to 5 years after the hurricane. The results are shown in Table 7 and Fig. 4. ing whether to allocate additional capital—especially if the perceived risk of additional hurricanes in an area is increas- ing due to climate change. The impact on property values and returns that we found go beyond any impact due to phys- 6 Conclusion ical damage to the properties. The loss in value appears to last up to 5 years after the hurricane makes landfall, and This paper examined the impact of hurricanes on proper- ties owned by institutional investors. It is the first study to is likely a result of higher risk premiums and lower tenant demand after the occurrence of a hurricane. examine all the significant hurricanes that have occurred Figure 4 Cumulative total 20% return change for quarters after 10% Hurricanes by property type 0% -10% -20% -30% -40% -50% -60% 4Q 8Q 12Q16Q 20Q Office Retail Apartment Industrial Hotel The impact of Hurricanes on the value of commercial real estate 137 Appendix Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) able Quarter and Location Dummy Variable Regression Results, yyyyq_19964 − 0.05524410 − 0.75 − 0.05545580 − 0.75 without (1) and with (2) Interaction Dummy Variables yyyyq_19971 − 0.05735300 − 0.78 − 0.05754380 − 0.78 yyyyq_19972 − 0.06629550 − 0.90 − 0.06648180 − 0.91 Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) yyyyq_19973 − 0.07319480 − 1.00 − 0.07338530 − 1.00 able yyyyq_19974 − 0.09193420 − 1.25 − 0.09210540 − 1.26 yyyyq_19852 0.00562370 0.06 0.00568200 0.06 yyyyq_19981 − 0.09811930 − 1.34 − 0.09827660 − 1.34 yyyyq_19853 0.01743420 0.17 0.01750870 0.17 yyyyq_19982 − 0.10344660 − 1.41 − 0.10361530 − 1.41 yyyyq_19854 − 0.03822030 − 0.38 − 0.03812980 − 0.38 yyyyq_19983 − 0.10550190 − 1.44 − 0.10566160 − 1.44 yyyyq_19861 − 0.07669100 − 0.78 − 0.07665380 − 0.78 yyyyq_19984 − 0.11681660 − 1.59 − 0.11696840 − 1.60 yyyyq_19862 − 0.06286220 − 0.66 − 0.06265060 − 0.66 yyyyq_19991 − 0.12166000 − 1.66 − 0.12183820 − 1.66 yyyyq_19863 − 0.05191480 − 0.56 − 0.05174670 − 0.56 yyyyq_19992 − 0.12534610 − 1.71 − 0.12551910 − 1.71 yyyyq_19864 − 0.02928690 − 0.32 − 0.02921820 − 0.32 yyyyq_19993 0.11601060 7.20 0.11622430 7.22 yyyyq_19871 − 0.10424150 − 1.36 − 0.10441380 − 1.36 yyyyq_19994 − 0.16960610 − 2.31 − 0.16978330 − 2.32 yyyyq_19872 − 0.09822290 − 1.29 − 0.09840280 − 1.29 yyyyq_20001 − 0.17470770 − 2.38 − 0.17487810 − 2.39 yyyyq_19873 − 0.09941110 − 1.30 − 0.09958360 − 1.30 yyyyq_20002 − 0.19271280 − 2.63 − 0.19286580 − 2.63 yyyyq_19874 − 0.11032860 − 1.45 − 0.11055000 − 1.45 yyyyq_20003 − 0.20101940 − 2.74 − 0.20116310 − 2.75 yyyyq_19881 − 0.14758310 − 1.98 − 0.14778930 − 1.99 yyyyq_20004 − 0.22048120 − 3.01 − 0.22063380 − 3.01 yyyyq_19882 − 0.15339120 − 2.07 − 0.15360660 − 2.07 yyyyq_20011 − 0.23665740 − 3.23 − 0.23681730 − 3.23 yyyyq_19883 − 0.16924690 − 2.28 − 0.16946530 − 2.28 yyyyq_20012 0.02847760 1.81 0.02858290 1.81 yyyyq_19884 − 0.21615170 − 2.91 − 0.21639850 − 2.91 yyyyq_20013 − 0.24567900 − 3.36 − 0.24583230 − 3.36 yyyyq_19891 − 0.23314630 − 3.14 − 0.23338880 − 3.14 yyyyq_20014 − 0.24292020 − 3.32 − 0.24308070 − 3.32 yyyyq_19892 − 0.24192040 − 3.25 − 0.24216700 − 3.25 yyyyq_20021 − 0.22537760 − 3.08 − 0.22556240 − 3.08 yyyyq_19894 − 0.32476270 − 4.37 − 0.32498710 − 4.38 yyyyq_20022 − 0.20951310 − 2.86 − 0.20973680 − 2.87 yyyyq_19901 − 0.33985180 − 4.59 − 0.34009540 − 4.60 yyyyq_20023 − 0.18865390 − 2.58 − 0.18887910 − 2.58 yyyyq_19902 − 0.36513480 − 4.93 − 0.36537880 − 4.94 yyyyq_20024 − 0.19805760 − 2.71 − 0.19830570 − 2.71 yyyyq_19903 − 0.37653540 − 5.09 − 0.37674520 − 5.10 yyyyq_20031 − 0.13908620 − 1.90 − 0.13933310 − 1.90 yyyyq_19904 − 0.38840840 − 5.26 − 0.38866490 − 5.26 yyyyq_20032 − 0.18820120 − 2.57 − 0.18844380 − 2.58 yyyyq_19911 − 0.38006220 − 5.15 − 0.38031870 − 5.15 yyyyq_20033 0.13966830 8.94 0.13677690 8.76 yyyyq_19912 − 0.38211360 − 5.18 − 0.38234440 − 5.19 yyyyq_20034 − 0.09703900 − 1.33 − 0.09726360 − 1.33 yyyyq_19913 − 0.36916340 − 5.01 − 0.36938500 − 5.01 yyyyq_20041 − 0.04389540 − 0.60 − 0.04412190 − 0.60 yyyyq_19914 − 0.32689450 − 4.43 − 0.32712330 − 4.44 yyyyq_20042 − 0.02613820 − 0.36 − 0.02634300 − 0.36 yyyyq_19921 − 0.31761600 − 4.30 − 0.31787370 − 4.31 yyyyq_20043 0.24147530 16.18 0.23826680 15.96 yyyyq_19922 − 0.28782890 − 3.91 − 0.28805340 − 3.91 yyyyq_20044 − 0.02147050 − 0.29 − 0.02163840 − 0.30 yyyyq_19923 − 0.00036270 − 0.02 − 0.00250760 − 0.14 yyyyq_20051 − 0.00318710 − 0.04 − 0.00335910 − 0.05 yyyyq_19924 − 0.21314360 − 2.90 − 0.21338870 − 2.90 yyyyq_20052 0.00280650 0.04 0.00269800 0.04 yyyyq_19931 − 0.20243990 − 2.75 − 0.20267600 − 2.76 yyyyq_20053 0.24307570 16.21 0.23842970 15.90 yyyyq_19932 − 0.17757460 − 2.41 − 0.17781810 − 2.42 yyyyq_20054 0.22614080 14.69 0.22146830 14.38 yyyyq_19933 − 0.16859040 − 2.29 − 0.16882150 − 2.30 yyyyq_20061 − 0.04951130 − 0.68 − 0.04963050 − 0.68 yyyyq_19934 − 0.15931240 − 2.17 − 0.15954960 − 2.17 yyyyq_20062 − 0.08639170 − 1.18 − 0.08649160 − 1.18 yyyyq_19941 − 0.15271660 − 2.08 − 0.15293420 − 2.08 yyyyq_20063 − 0.12472230 − 1.71 − 0.12482910 − 1.71 yyyyq_19942 − 0.14924980 − 2.03 − 0.14942790 − 2.03 yyyyq_20064 − 0.24055330 − 3.29 − 0.24066160 − 3.29 yyyyq_19943 − 0.13319260 − 1.81 − 0.13335410 − 1.82 yyyyq_20071 − 0.33102210 − 4.53 − 0.33114730 − 4.53 yyyyq_19944 − 0.12889990 − 1.75 − 0.12904220 − 1.76 yyyyq_20072 − 0.41346090 − 5.66 − 0.41359250 − 5.66 yyyyq_19951 − 0.13109730 − 1.78 − 0.13124270 − 1.79 yyyyq_20073 − 0.47112360 − 6.44 − 0.47126680 − 6.45 yyyyq_19952 − 0.12176330 − 1.66 − 0.12190880 − 1.66 yyyyq_20074 − 0.50349110 − 6.89 − 0.50364190 − 6.89 yyyyq_19953 − 0.10507050 − 1.43 − 0.10521510 − 1.43 yyyyq_20081 − 0.50934650 − 6.97 − 0.50950560 − 6.97 yyyyq_19954 − 0.07552310 − 1.03 − 0.07570550 − 1.03 yyyyq_20082 − 0.50359730 − 6.89 − 0.50376680 − 6.89 yyyyq_19961 − 0.06993230 − 0.95 − 0.07014840 − 0.95 yyyyq_20083 − 0.22405520 − 15.09 − 0.22685090 − 15.28 yyyyq_19962 − 0.05570120 − 0.76 − 0.05590950 − 0.76 yyyyq_20084 − 0.40210170 − 5.50 − 0.40231170 − 5.51 yyyyq_19963 0.19748380 11.60 0.19621510 11.52 yyyyq_20091 − 0.32388370 − 4.43 − 0.32411030 − 4.43 138 J. D. Fisher, S. R. Rutledge Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) able able yyyyq_20092 − 0.22302120 − 3.05 − 0.22326680 − 3.05 CBSAor- 0.03630710 1.38 0.03803020 1.45 Div_11460 yyyyq_20093 − 0.17099110 − 2.34 − 0.17125030 − 2.34 CBSAor- 0.12314920 2.33 0.12465180 2.36 yyyyq_20094 − 0.11369210 − 1.56 − 0.11396770 − 1.56 Div_11700 yyyyq_20101 − 0.10318210 − 1.41 − 0.10352150 − 1.42 CBSAor- 0.06432920 0.59 0.06539830 0.60 yyyyq_20102 − 0.07778580 − 1.06 − 0.07813050 − 1.07 Div_11820 yyyyq_20103 − 0.10807030 − 1.48 − 0.10842040 − 1.48 CBSAor- − 0.00727200 − 0.14 − 0.00642160 − 0.12 yyyyq_20104 − 0.12864170 − 1.76 − 0.12898990 − 1.76 Div_12020 yyyyq_20111 − 0.13639330 − 1.87 − 0.13679160 − 1.87 CBSAor- 0.01238940 0.59 0.01199300 0.57 yyyyq_20112 − 0.13942410 − 1.91 − 0.13979420 − 1.91 Div_12060 yyyyq_20113 0.12938080 8.50 0.12793970 8.41 CBSAor- − 0.02782970 − 0.45 − 0.03054300 − 0.49 Div_12140 yyyyq_20114 − 0.15065190 − 2.06 − 0.15098020 − 2.07 CBSAor- 0.00310030 0.04 0.00591370 0.08 yyyyq_20121 − 0.13481750 − 1.84 − 0.13509460 − 1.85 Div_12220 yyyyq_20122 − 0.12937730 − 1.77 − 0.12960170 − 1.77 CBSAor- − 0.03616970 − 0.94 − 0.03433420 − 0.89 yyyyq_20123 − 0.13846560 − 1.89 − 0.13870390 − 1.90 Div_12260 yyyyq_20124 0.12163220 8.03 0.11938350 7.88 CBSAor- 0.05425160 0.80 0.05724100 0.84 yyyyq_20131 − 0.10180570 − 1.39 − 0.10202480 − 1.40 Div_12300 yyyyq_20132 − 0.09682420 − 1.32 − 0.09703950 − 1.33 CBSAor- 0.04505750 2.12 0.04617740 2.17 yyyyq_20133 − 0.09941950 − 1.36 − 0.09961560 − 1.36 Div_12420 yyyyq_20134 − 0.11104380 − 1.52 − 0.11123710 − 1.52 CBSAor- 0.02056440 0.65 0.02275120 0.72 Div_12540 yyyyq_20141 − 0.08588600 − 1.17 − 0.08608080 − 1.18 CBSAor- 0.02782890 1.30 0.02776190 1.30 yyyyq_20142 − 0.09618470 − 1.32 − 0.09634630 − 1.32 Div_12580 yyyyq_20143 − 0.11351450 − 1.55 − 0.11364350 − 1.56 CBSAor- − 0.03350870 − 0.33 − 0.03305450 − 0.32 yyyyq_20144 − 0.12322040 − 1.69 − 0.12336310 − 1.69 Div_12620 yyyyq_20151 − 0.16607000 − 2.27 − 0.16621280 − 2.27 CBSAor- − 0.01949730 − 0.52 − 0.01708590 − 0.45 yyyyq_20152 − 0.12684650 − 1.74 − 0.12698750 − 1.74 Div_12700 yyyyq_20153 − 0.14955400 − 2.05 − 0.14967780 − 2.05 CBSAor- 0.13639550 2.18 0.13366010 2.14 yyyyq_20154 − 0.15901450 − 2.18 − 0.15883410 − 2.17 Div_12860 yyyyq_20161 − 0.14654260 − 2.00 − 0.14637490 − 2.00 CBSAor- − 0.01246580 − 0.44 − 0.01065380 − 0.38 Div_12940 yyyyq_20162 − 0.14285090 − 1.95 − 0.14267860 − 1.95 CBSAor- − 0.38808790 − 4.02 − 0.38700080 − 4.01 yyyyq_20163 − 0.14493320 − 1.98 − 0.14475330 − 1.98 Div_13140 yyyyq_20164 0.10729300 7.12 0.10197400 6.77 CBSAor- 0.11265990 1.77 0.11392330 1.79 yyyyq_20171 − 0.14266010 − 1.95 − 0.14251270 − 1.95 Div_13300 yyyyq_20172 − 0.14273490 − 1.95 − 0.14256780 − 1.95 CBSAor- − 0.20068680 − 1.22 − 0.19881110 − 1.21 yyyyq_20173 0.10764440 7.26 0.10262960 6.92 Div_13380 CBSAor- 0.08967670 1.80 0.09062860 1.82 CBSAor- − 0.40636040 − 4.82 − 0.40553000 − 4.81 Div_10500 Div_13780 CBSAor- − 0.08396940 − 2.13 − 0.08340060 − 2.12 CBSAor- − 0.01877840 − 0.74 − 0.01665950 − 0.66 Div_10580 Div_13820 CBSAor- − 0.02741100 − 1.09 − 0.02579920 − 1.03 CBSAor- 0.05279690 0.88 0.05385080 0.89 Div_10740 Div_13980 CBSAor- 0.05636630 2.31 0.05489380 2.25 CBSAor- − 0.00011130 0.00 0.00068930 0.02 Div_10900 Div_14010 CBSAor- − 0.09550510 − 1.33 − 0.09376300 − 1.31 CBSAor- − 0.07013260 − 0.92 − 0.06860190 − 0.90 Div_11020 Div_14020 CBSAor- − 0.11377710 − 2.56 − 0.11245190 − 2.53 CBSAor- 0.03582320 0.54 0.03773570 0.57 Div_11100 Div_14100 CBSAor- − 0.01318390 − 0.07 − 0.01252100 − 0.06 CBSAor- 0.00875670 0.15 0.01053660 0.19 Div_11180 Div_14260 CBSAor- 0.05509590 2.60 0.05525730 2.61 CBSAor- 0.03990870 1.86 0.04158780 1.94 Div_11244 Div_14454 CBSAor- − 0.00872410 − 0.31 − 0.00736510 − 0.26 Div_11260 The impact of Hurricanes on the value of commercial real estate 139 Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) able able CBSAor- 0.01384760 0.62 0.01343010 0.60 CBSAor- − 0.05582420 − 2.30 − 0.05413760 − 2.23 Div_14500 Div_17460 CBSAor- 0.29152510 1.77 0.27989230 1.70 CBSAor- 0.06682720 1.15 0.06857920 1.18 Div_14540 Div_17660 CBSAor- 0.01029260 0.45 0.01309990 0.57 CBSAor- − 0.04524050 − 0.99 − 0.04245070 − 0.93 Div_14860 Div_17780 CBSAor- − 0.08061200 − 1.43 − 0.08074740 − 1.43 CBSAor- 0.01952470 0.80 0.02175420 0.90 Div_15180 Div_17820 CBSAor- − 0.13339490 − 1.45 − 0.13295800 − 1.45 CBSAor- − 0.01106770 − 0.22 − 0.00843200 − 0.17 Div_15260 Div_17860 CBSAor- − 0.13551330 − 3.28 − 0.13490930 − 3.26 CBSAor- 0.02982490 1.15 0.03077250 1.19 Div_15380 Div_17900 CBSAor- − 0.21293390 − 2.32 − 0.21188710 − 2.31 CBSAor- − 0.14734160 − 1.94 − 0.14332160 − 1.89 Div_15460 Div_17980 CBSAor- 0.03020140 0.48 0.03120030 0.50 CBSAor- − 0.03751700 − 1.71 − 0.03623940 − 1.65 Div_15660 Div_18140 CBSAor- − 0.06368730 − 1.14 − 0.06798280 − 1.22 CBSAor- − 0.12806460 − 2.40 − 0.12453000 − 2.33 Div_15680 Div_18180 CBSAor- 0.01487390 0.70 0.01653080 0.77 CBSAor- − 0.10770200 − 0.54 − 0.10701360 − 0.53 Div_15764 Div_18420 CBSAor- 0.02686060 1.22 0.02632430 1.19 CBSAor- 0.06517690 2.21 0.06647750 2.26 Div_15804 Div_18580 CBSAor- − 0.11706110 − 1.44 − 0.12072400 − 1.49 CBSAor- − 0.08362120 − 1.48 − 0.08269440 − 1.46 Div_15820 Div_18880 CBSAor- − 0.08229190 − 0.90 − 0.08127590 − 0.89 CBSAor- 0.00838220 0.13 0.00929930 0.15 Div_15940 Div_18900 CBSAor- 0.04378260 1.83 0.04517490 1.89 CBSAor- 0.01901320 0.90 0.01899260 0.90 Div_15980 Div_19124 CBSAor- − 0.50701390 − 4.66 − 0.50643830 − 4.65 CBSAor- − 0.10346700 − 1.62 − 0.10246010 − 1.61 Div_16060 Div_19140 CBSAor- − 0.15942930 − 2.03 − 0.15785000 − 2.01 CBSAor- 0.00291180 0.04 0.00369330 0.06 Div_16180 Div_19220 CBSAor- − 0.08494010 − 0.97 − 0.08435320 − 0.96 CBSAor- − 0.07833080 − 1.92 − 0.07756310 − 1.90 Div_16300 Div_19340 CBSAor- 0.04296480 0.84 0.04053850 0.80 CBSAor- − 0.01826220 − 0.52 − 0.01701250 − 0.48 Div_16540 Div_19380 CBSAor- − 0.04801020 − 0.72 − 0.04734270 − 0.71 CBSAor- 0.01064810 0.26 0.00908340 0.22 Div_16580 Div_19500 CBSAor- 0.06947770 2.52 0.07144950 2.59 CBSAor- 0.04641600 1.39 0.04804730 1.44 Div_16700 Div_19660 CBSAor- 0.01860070 0.86 0.01963120 0.91 CBSAor- 0.04525330 2.13 0.04674530 2.20 Div_16740 Div_19740 CBSAor- − 0.01645690 − 0.46 − 0.01317210 − 0.37 CBSAor- − 0.07783360 − 2.35 − 0.07648890 − 2.31 Div_16820 Div_19780 CBSAor- 0.09295770 1.64 0.09378300 1.66 CBSAor- 0.03239320 1.41 0.03166430 1.38 Div_16860 Div_19804 CBSAor- 0.01041280 0.49 0.01039410 0.49 CBSAor- 0.05819300 0.63 0.05849880 0.64 Div_16974 Div_20260 CBSAor- − 0.17558810 − 2.38 − 0.17617770 − 2.39 CBSAor- 0.03996940 1.74 0.04107220 1.79 Div_17020 Div_20500 CBSAor- − 0.02496500 − 1.14 − 0.02533920 − 1.16 CBSAor- − 0.03546880 − 0.79 − 0.02996150 − 0.66 Div_17140 Div_20524 CBSAor- − 0.11153780 − 0.56 − 0.11087520 − 0.55 CBSAor- 0.00498100 0.11 0.01545490 0.35 Div_17200 Div_20780 140 J. D. Fisher, S. R. Rutledge Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) able able CBSAor- − 0.01872830 − 0.29 − 0.01771620 − 0.28 CBSAor- 0.10069130 1.04 0.10149390 1.05 Div_20900 Div_24220 CBSAor- − 0.01737660 − 0.74 − 0.01839430 − 0.78 CBSAor- 0.03257670 0.42 0.03341970 0.43 Div_20994 Div_24300 CBSAor- − 0.01421310 − 0.47 − 0.01473380 − 0.49 CBSAor- 0.00993710 0.25 0.01191070 0.31 Div_21340 Div_24340 CBSAor- − 0.31240480 − 5.84 − 0.31258560 − 5.85 CBSAor- 0.01709620 0.27 0.01849030 0.29 Div_21420 Div_24540 CBSAor- 0.25771070 1.28 0.25717690 1.28 CBSAor- 0.01053820 0.42 0.01212440 0.48 Div_21460 Div_24660 CBSAor- 0.02442750 0.60 0.02520400 0.62 CBSAor- 0.06961350 0.89 0.07231600 0.92 Div_21660 Div_24780 CBSAor- − 0.08935260 − 1.46 − 0.08893760 − 1.45 CBSAor- 0.02930090 1.15 0.03144370 1.23 Div_21780 Div_24860 CBSAor- 0.05149970 0.82 0.04866950 0.78 CBSAor- 0.00469960 0.07 0.00615690 0.09 Div_22020 Div_25060 CBSAor- − 0.21965260 − 3.30 − 0.22092540 − 3.32 CBSAor- 0.02341100 0.37 0.02504250 0.39 Div_22140 Div_25180 CBSAor- − 0.09044250 − 2.86 − 0.08956520 − 2.83 CBSAor- − 0.00325570 − 0.14 − 0.00425250 − 0.18 Div_22180 Div_25420 CBSAor- − 0.04999760 − 0.95 − 0.04962940 − 0.94 CBSAor- 0.00800100 0.10 0.00910590 0.12 Div_22220 Div_25500 CBSAor- − 0.11396160 − 2.33 − 0.11872020 − 2.43 CBSAor- − 0.02058250 − 0.87 − 0.01891210 − 0.80 Div_22280 Div_25540 CBSAor- 0.00375700 0.03 0.00456880 0.04 CBSAor- − 0.06269930 − 0.74 − 0.06172510 − 0.73 Div_22380 Div_25620 CBSAor- − 0.04576900 − 1.12 − 0.04521160 − 1.11 CBSAor- 0.18015570 2.88 0.18098160 2.89 Div_22420 Div_25860 CBSAor- − 0.07547880 − 1.16 − 0.07586360 − 1.16 CBSAor- 0.04843830 1.02 0.04993700 1.05 Div_22500 Div_25900 CBSAor- − 0.12347620 − 1.86 − 0.12200010 − 1.83 CBSAor- 0.04400040 0.93 0.04560000 0.96 Div_22520 Div_25940 CBSAor- − 1.07313000 − 7.52 − 1.07234300 − 7.51 CBSAor- − 0.19753070 − 2.68 − 0.19662010 − 2.67 Div_22660 Div_26300 CBSAor- 0.04309670 2.02 0.04382580 2.06 CBSAor- − 0.09820290 − 1.12 − 0.09708420 − 1.11 Div_22744 Div_26380 CBSAor- − 0.14298820 − 0.71 − 0.14269820 − 0.71 CBSAor- 0.04277830 2.02 0.04327510 2.04 Div_22800 Div_26420 CBSAor- − 0.04834980 − 0.69 − 0.04673860 − 0.67 CBSAor- − 0.21304570 − 2.09 − 0.21279880 − 2.09 Div_22900 Div_26580 CBSAor- − 0.09769120 − 2.48 − 0.09800110 − 2.49 CBSAor- − 0.06389590 − 1.90 − 0.06235820 − 1.85 Div_23060 Div_26620 CBSAor- 0.02338470 1.08 0.02292470 1.06 CBSAor- 0.03734950 0.44 0.04074800 0.48 Div_23104 Div_26660 CBSAor- 0.04362630 1.20 0.04701840 1.30 CBSAor- − 0.00501170 − 0.23 − 0.00716010 − 0.33 Div_23420 Div_26900 CBSAor- 0.06284370 1.76 0.06519350 1.82 CBSAor- − 0.01299940 − 0.29 − 0.01180170 − 0.27 Div_23540 Div_26980 CBSAor- − 0.01498980 − 0.25 − 0.01331430 − 0.22 CBSAor- 0.00105320 0.03 − 0.00103830 − 0.03 Div_23580 Div_27140 CBSAor- 0.05163210 1.98 0.04933540 1.89 CBSAor- − 0.03885350 − 0.68 − 0.03732500 − 0.65 Div_23844 Div_27180 CBSAor- − 0.12629830 − 0.45 − 0.12541080 − 0.44 CBSAor- − 0.00500550 − 0.22 − 0.00376920 − 0.17 Div_24020 Div_27260 The impact of Hurricanes on the value of commercial real estate 141 Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) able able CBSAor- − 0.12389810 − 1.90 − 0.12207590 − 1.88 CBSAor- 0.05458880 0.80 0.05768350 0.85 Div_27340 Div_29940 CBSAor- − 0.03316060 − 0.49 − 0.03322090 − 0.49 CBSAor- 0.01928300 0.46 0.01708390 0.41 Div_27540 Div_30140 CBSAor- − 0.02188440 − 0.64 − 0.02590390 − 0.76 CBSAor- 0.10653890 3.32 0.10533770 3.28 Div_27600 Div_30220 CBSAor- 0.03047900 0.77 0.03205990 0.81 CBSAor- − 0.10034160 − 1.44 − 0.10199680 − 1.46 Div_27620 Div_30380 CBSAor- − 0.18996690 − 1.86 − 0.18481280 − 1.81 CBSAor- − 0.01952060 − 0.67 − 0.01839540 − 0.64 Div_27740 Div_30460 CBSAor- 0.01473010 0.37 0.01913670 0.48 CBSAor- − 0.30745730 − 4.62 − 0.30630890 − 4.60 Div_27940 Div_30620 CBSAor- 0.01742060 0.54 0.01920880 0.59 CBSAor- − 0.09934910 − 3.24 − 0.09773410 − 3.19 Div_27980 Div_30780 CBSAor- 0.03386090 0.89 0.03585760 0.95 CBSAor- − 0.12101020 − 1.59 − 0.11960550 − 1.58 Div_28020 Div_30980 CBSAor- 0.00638570 0.29 0.00617970 0.28 CBSAor- 0.07912780 3.75 0.07865070 3.73 Div_28140 Div_31084 CBSAor- 0.04314310 1.35 0.04701960 1.47 CBSAor- 0.04458550 2.01 0.04178470 1.88 Div_28180 Div_31140 CBSAor- 0.07350300 1.76 0.07546730 1.80 CBSAor- − 0.16127920 − 1.13 − 0.16062280 − 1.13 Div_28420 Div_31340 CBSAor- − 0.22172320 − 3.74 − 0.21992460 − 3.71 CBSAor- − 0.15519560 − 3.36 − 0.15389440 − 3.33 Div_28540 Div_31420 CBSAor- − 0.07205850 − 0.86 − 0.07077190 − 0.84 CBSAor- 0.02559110 0.37 0.02665190 0.38 Div_28580 Div_31460 CBSAor- − 0.00999690 − 0.25 − 0.00848790 − 0.21 CBSAor- − 0.02551110 − 0.44 − 0.02343280 − 0.41 Div_28660 Div_31540 CBSAor- 0.04783380 0.76 0.04884910 0.78 CBSAor- − 0.02743750 − 1.05 − 0.02731570 − 1.05 Div_28700 Div_31700 CBSAor- − 0.05271780 − 0.57 − 0.05129700 − 0.56 CBSAor- − 0.03926970 − 0.86 − 0.03718140 − 0.82 Div_28740 Div_31740 CBSAor- 0.02708640 1.10 0.02908350 1.18 CBSAor- − 0.15683640 − 2.25 − 0.15676860 − 2.25 Div_28940 Div_31820 CBSAor- 0.10401280 2.23 0.10502040 2.25 CBSAor- − 0.01673650 − 0.27 − 0.01575960 − 0.26 Div_29100 Div_32180 CBSAor- 0.01868120 0.35 0.01690060 0.32 CBSAor- − 0.05165460 − 1.57 − 0.05359980 − 1.63 Div_29180 Div_32580 CBSAor- 0.02833120 0.81 0.02940900 0.84 CBSAor- − 0.03747760 − 0.49 − 0.03677210 − 0.48 Div_29200 Div_32780 CBSAor- 0.00109660 0.05 0.00130480 0.06 CBSAor- − 0.02190720 − 1.01 − 0.02255240 − 1.04 Div_29404 Div_32820 CBSAor- 0.06817860 1.87 0.06565440 1.80 CBSAor- − 0.25606530 − 2.79 − 0.25770190 − 2.81 Div_29420 Div_32860 CBSAor- 0.00284040 0.10 0.00354550 0.12 CBSAor- 0.06046110 2.83 0.06050180 2.83 Div_29460 Div_33124 CBSAor- 0.10624550 2.08 0.10737370 2.10 CBSAor- − 0.02754650 − 1.19 − 0.02707490 − 1.17 Div_29540 Div_33340 CBSAor- − 0.01207380 − 0.29 − 0.00990320 − 0.24 CBSAor- − 0.00195530 − 0.09 − 0.00159080 − 0.07 Div_29620 Div_33460 CBSAor- − 0.00684160 − 0.10 − 0.00546660 − 0.08 CBSAor- 0.14269180 0.71 0.14347310 0.71 Div_29740 Div_33500 CBSAor- 0.00649660 0.30 0.00695230 0.32 CBSAor- − 0.04282600 − 0.82 − 0.04176980 − 0.80 Div_29820 Div_33660 142 J. D. Fisher, S. R. Rutledge Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) able able CBSAor- 0.06599780 0.84 0.06370190 0.81 CBSAor- − 0.21518380 − 2.65 − 0.21478830 − 2.65 Div_33700 Div_36980 CBSAor- − 0.16933260 − 2.36 − 0.16615040 − 2.32 CBSAor- − 0.53151670 − 3.72 − 0.52562270 − 3.68 Div_33860 Div_37060 CBSAor- 0.00843120 0.39 0.01025840 0.47 CBSAor- 0.03864310 1.65 0.04008420 1.71 Div_33874 Div_37100 CBSAor- − 0.18511220 − 4.77 − 0.18291590 − 4.72 CBSAor- − 0.11348630 − 1.63 − 0.11240190 − 1.61 Div_34100 Div_37120 CBSAor- 0.22491730 3.60 0.22584830 3.61 CBSAor- − 0.01232990 − 0.29 − 0.01116930 − 0.26 Div_34340 Div_37140 CBSAor- 0.00598390 0.15 0.00723470 0.18 CBSAor- 0.14153230 2.08 0.14017350 2.06 Div_34820 Div_37220 CBSAor- 0.12034050 3.87 0.12212600 3.92 CBSAor- − 0.02969700 − 1.09 − 0.02812320 − 1.03 Div_34900 Div_37340 CBSAor- 0.05034720 1.99 0.05212590 2.06 CBSAor- − 0.29366090 − 2.51 − 0.29293500 − 2.50 Div_34940 Div_37460 CBSAor- 0.06662420 3.05 0.06761180 3.10 CBSAor- 0.02285230 0.37 0.02383950 0.39 Div_34980 Div_37660 CBSAor- − 0.03031980 − 1.27 − 0.02831350 − 1.19 CBSAor- 0.08406940 2.24 0.08423190 2.24 Div_35004 Div_37860 CBSAor- − 0.02038860 − 0.94 − 0.01884520 − 0.87 CBSAor- − 0.11660080 − 3.26 − 0.11566810 − 3.23 Div_35084 Div_37900 CBSAor- − 0.07603710 − 2.64 − 0.07457310 − 2.59 CBSAor- 0.01040860 0.46 0.01181320 0.53 Div_35300 Div_37964 CBSAor- − 0.02589330 − 0.94 − 0.02150100 − 0.78 CBSAor- 0.02744490 1.29 0.02833760 1.34 Div_35380 Div_38060 CBSAor- − 0.05464280 − 0.27 − 0.05515830 − 0.27 CBSAor- − 0.03190040 − 0.31 − 0.02985990 − 0.29 Div_35440 Div_38240 CBSAor- 0.05540560 2.62 0.05606180 2.65 CBSAor- − 0.02643720 − 1.15 − 0.02429790 − 1.06 Div_35614 Div_38300 CBSAor- − 0.30360780 − 1.85 − 0.30170010 − 1.84 CBSAor- 0.09858810 1.72 0.10062660 1.75 Div_35660 Div_38820 CBSAor- − 0.13614880 − 4.15 − 0.13530930 − 4.13 CBSAor- − 0.01334860 − 0.45 − 0.01174550 − 0.40 Div_35840 Div_38860 CBSAor- − 0.06334620 − 1.23 − 0.06059340 − 1.18 CBSAor- 0.06889040 3.23 0.06846580 3.21 Div_35980 Div_38900 CBSAor- 0.07295630 3.44 0.07284420 3.43 CBSAor- 0.01473850 0.56 0.01657220 0.63 Div_36084 Div_38940 CBSAor- − 0.08267180 − 1.85 − 0.08121240 − 1.81 CBSAor- − 0.00619740 − 0.27 − 0.00605800 − 0.27 Div_36100 Div_39300 CBSAor- 0.23365600 2.55 0.23478740 2.56 CBSAor- 0.09389480 1.53 0.09138870 1.49 Div_36220 Div_39340 CBSAor- 0.13148810 2.64 0.13272350 2.66 CBSAor- 0.04489030 1.35 0.04709460 1.42 Div_36260 Div_39460 CBSAor- − 0.01986660 − 0.81 − 0.02063800 − 0.84 CBSAor- − 0.01156810 − 0.30 − 0.01200960 − 0.32 Div_36420 Div_39540 CBSAor- − 0.00042240 − 0.02 0.00169160 0.08 CBSAor- 0.06639780 2.04 0.06411760 1.97 Div_39580 Div_36500 CBSAor- − 0.02593890 − 0.33 − 0.02474270 − 0.32 CBSAor- 0.00694950 0.27 0.00835070 0.32 Div_39660 Div_36540 CBSAor- 0.02341580 0.74 0.02122360 0.67 CBSAor- 0.03364750 1.57 0.03475590 1.62 Div_39740 Div_36740 CBSAor- 0.03551000 1.58 0.03544870 1.58 CBSAor- − 0.13957460 − 2.00 − 0.13741170 − 1.97 Div_39900 Div_36900 The impact of Hurricanes on the value of commercial real estate 143 Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) able able CBSAor- − 0.48142420 − 5.25 − 0.48094810 − 5.24 CBSAor- − 0.10999160 − 2.95 − 0.11161480 − 2.99 Div_39980 Div_42540 CBSAor- − 0.00960210 − 0.42 − 0.00962380 − 0.42 CBSAor- 0.08228880 3.89 0.08265010 3.91 Div_40060 Div_42644 CBSAor- 0.10623210 5.01 0.10475170 4.94 CBSAor- 0.11826030 1.51 0.12133860 1.55 Div_40140 Div_42680 CBSAor- 0.06354130 1.02 0.06073260 0.97 CBSAor- 0.21379830 3.28 0.21204220 3.26 Div_40220 Div_43100 CBSAor- 0.00437450 0.10 0.00145920 0.03 CBSAor- − 0.21161600 − 2.51 − 0.21120770 − 2.51 Div_40300 Div_43140 CBSAor- 0.01717140 0.30 0.01816500 0.32 CBSAor- 0.00902390 0.22 0.01025660 0.25 Div_40380 Div_43300 CBSAor- − 0.03750120 − 0.81 − 0.03664120 − 0.79 CBSAor- − 0.09569160 − 1.64 − 0.09671040 − 1.66 Div_40420 Div_43340 CBSAor- 0.09052400 3.39 0.09240800 3.46 CBSAor- 0.01170800 0.54 0.01398840 0.65 Div_40484 Div_43524 CBSAor- − 0.00975770 − 0.45 − 0.00825920 − 0.38 CBSAor- 0.04961740 0.63 0.05082830 0.65 Div_40900 Div_43580 CBSAor- 0.01050490 0.16 0.01076980 0.17 CBSAor- 0.05420990 0.90 0.05518720 0.92 Div_41060 Div_43620 CBSAor- 0.00504310 0.23 0.00521270 0.24 CBSAor- − 0.09273170 − 2.32 − 0.09252370 − 2.32 Div_41180 Div_43780 CBSAor- 0.02857930 0.20 0.02940910 0.21 CBSAor- 0.05813520 1.18 0.05538620 1.12 Div_41420 Div_43900 CBSAor- 0.05863430 1.46 0.06020630 1.50 CBSAor- 0.18025260 3.90 0.17755230 3.84 Div_41460 Div_44060 CBSAor- 0.01377610 0.41 0.01530860 0.46 CBSAor- − 0.21345020 − 4.91 − 0.21152660 − 4.86 Div_41500 Div_44140 CBSAor- − 0.13790690 − 2.02 − 0.13519050 − 1.98 CBSAor- 0.23211850 2.96 0.23327410 2.97 Div_41540 Div_44180 CBSAor- 0.03160430 1.41 0.03267340 1.46 CBSAor- − 0.04012310 − 0.49 − 0.03696890 − 0.46 Div_41620 Div_44260 CBSAor- 0.01377140 0.63 0.01416410 0.65 CBSAor- − 0.52972930 − 4.14 − 0.52914840 − 4.13 Div_41700 Div_44340 CBSAor- 0.05250510 2.47 0.05335130 2.51 CBSAor- − 0.09630910 − 1.80 − 0.09502500 − 1.78 Div_41740 Div_44420 CBSAor- 0.09862270 4.61 0.10050470 4.70 CBSAor- 0.07183550 2.95 0.06971310 2.87 Div_41884 Div_44700 CBSAor- 0.05760110 2.70 0.05777720 2.71 CBSAor- − 0.14841490 − 1.89 − 0.15292740 − 1.95 Div_41940 Div_45060 CBSAor- 0.03042880 1.00 0.03198710 1.05 CBSAor- 0.12466630 5.53 0.12220060 5.42 Div_42020 Div_45104 CBSAor- 0.08786340 3.74 0.09058530 3.85 CBSAor- 0.02607360 0.85 0.02820450 0.92 Div_42034 Div_45220 CBSAor- 0.02538300 0.62 0.02660410 0.65 CBSAor- 0.02279310 1.06 0.02464630 1.14 Div_42100 Div_45300 CBSAor- 0.03980890 1.39 0.04228720 1.48 CBSAor- − 0.10874780 − 1.39 − 0.10776200 − 1.38 Div_42140 Div_45460 CBSAor- 0.03147910 1.16 0.03228720 1.19 CBSAor- − 0.06602140 − 1.03 − 0.06474480 − 1.02 Div_42200 Div_45500 CBSAor- 0.08286040 3.16 0.08476810 3.23 CBSAor- 0.82549570 12.67 0.82661550 12.69 Div_42220 Div_45520 CBSAor- − 0.04732210 − 1.11 − 0.04610870 − 1.08 CBSAor- 0.09185280 1.13 0.09509510 1.17 Div_42340 Div_45540 144 J. D. Fisher, S. R. Rutledge Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) Dummy vari- Coefficient (1) t-stat (1) Coefficient (2) t-stat (2) able able CBSAor- − 0.00891690 − 0.24 − 0.00777810 − 0.21 CBSAor- − 0.38295830 − 4.17 − 0.38173280 − 4.16 Div_45780 Div_48940 CBSAor- − 0.01729470 − 0.37 − 0.01296280 − 0.28 CBSAor- 0.06046340 1.04 0.06273630 1.08 Div_45820 Div_49020 CBSAor- 0.07847140 0.67 0.07881730 0.67 CBSAor- − 0.09769220 − 2.13 − 0.09654470 − 2.11 Div_45860 Div_49180 CBSAor- 0.06496440 2.68 0.06584670 2.72 CBSAor- − 0.02457440 − 1.03 − 0.02524680 − 1.06 Div_45940 Div_49340 CBSAor- 0.08125420 2.32 0.08287790 2.36 CBSAor- − 0.03406850 − 0.89 − 0.03230780 − 0.84 Div_46020 Div_49420 CBSAor- − 0.02807500 − 1.06 − 0.02755890 − 1.04 CBSAor- − 0.08854540 − 2.06 − 0.08782240 − 2.05 Div_46060 Div_49620 CBSAor- − 0.00146830 − 0.06 − 0.00080000 − 0.03 CBSAor- 0.00583120 0.09 0.00682860 0.11 Div_46140 Div_49700 CBSAor- − 0.08106300 − 0.92 − 0.07972340 − 0.91 CBSAor- 0.13135090 1.73 0.13219290 1.74 Div_46180 Div_49740 CBSAor- − 0.00033170 − 0.01 0.00064310 0.01 CBSAor- − 0.10569670 − 1.87 − 0.10385900 − 1.84 Div_46220 Div_49780 CBSAor- 0.07008260 1.16 0.07127200 1.18 Div_46300 CBSAor- − 0.54376900 − 5.93 − 0.54250340 − 5.91 Div_46500 Open Access This article is licensed under a Creative Commons Attri- CBSAor- − 0.02806810 − 1.04 − 0.02612630 − 0.97 bution 4.0 International License, which permits use, sharing, adapta- Div_46520 tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, CBSAor- 0.12283970 1.96 0.12013270 1.92 provide a link to the Creative Commons licence, and indicate if changes Div_46540 were made. The images or other third party material in this article are CBSAor- − 0.15036310 − 1.98 − 0.14980330 − 1.97 included in the article’s Creative Commons licence, unless indicated Div_46660 otherwise in a credit line to the material. If material is not included in CBSAor- 0.04154590 1.63 0.04094140 1.61 the article’s Creative Commons licence and your intended use is not Div_46700 permitted by statutory regulation or exceeds the permitted use, you will CBSAor- − 0.06045500 − 1.30 − 0.05847260 − 1.25 need to obtain permission directly from the copyright holder. To view a Div_46740 copy of this licence, visit http://cr eativ ecommons. or g/licen ses/ b y/4.0/ . CBSAor- 0.00116330 0.05 0.00308160 0.13 Div_47260 CBSAor- 0.37252660 5.95 0.37317330 5.97 Div_47300 References CBSAor- − 0.04284580 − 1.91 − 0.04112840 − 1.83 Div_47664 Bleich, Donald. 2003. The Reaction of Multifamily Capitalization CBSAor- 0.03246550 1.54 0.03463630 1.64 Rates to Natural Disasters. Journal of Real Estate Research 25 Div_47894 (2): 133–144. Graham, Jr., J. Edward, and William W. Hall, Jr. 2001. Hurricanes, CBSAor- − 0.04156440 − 0.95 − 0.04086620 − 0.93 Housing Market Activity, and Coastal Real Estate Values. The Div_47940 Appraisal Journal 69 (4): 379–387. CBSAor- 0.03875110 1.80 0.04052880 1.88 Graham, Edward, William Hall,  and Peter Schuhmann. 2007. Hur- Div_48424 ricanes, Catastrophic Risk, and Real Estate Market Recovery. CBSAor- − 0.03747680 − 0.70 − 0.03823650 − 0.72 Journal of Real Estate Portfolio Management 13 (3): 179–190. Div_48620 Morgan, Ash. 2007. The Impact of Hurricane Ivan on Expected Flood CBSAor- 0.05614910 1.22 0.05692420 1.24 Losses, Perceived Flood Risk, and Property Values. Journal of Div_48660 Housing Research 16 (1): 47–60. CBSAor- − 0.49116150 − 6.85 − 0.49072090 − 6.85 Salter, Sean P., and Ernest W. King. 2009. Price Adjustment and Div_48780 Liquidity in a Residential Real Estate Market with an Acceler- ated Information Cascade. Journal of Real Estate Research 31 CBSAor- − 0.01527400 − 0.64 − 0.01637680 − 0.68 (4): 421–454. Div_48864 Simmons, Kevin M., Jamie Brown Kruse, and Douglas A. Smith. 2002. CBSAor- 0.01675230 0.39 0.01937340 0.45 Valuing Mitigation: Real Estate Market Responses to Hurricane Div_48900 The impact of Hurricanes on the value of commercial real estate 145 Loss Reduction Measures. Southern Economic Journal 68 (3): Sara R. Rutledge Ms. Rutledge is the Founder and Principal Econo- 660–671. mist of SRR Consulting. She provides expert research and analysis on macroeconomic and real estate topics for a variety of public and private sector clients. Her past and current consulting projects include invest- Publisher’s Note Springer Nature remains neutral with regard to ment strategy white papers, real estate market research and analysis, and jurisdictional claims in published maps and institutional affiliations. report content creation and management. Ms. Rutledge was previously the Managing Director of Real Estate Products at StratoDem Analytics, an early-stage data science firm delivering market intelligence tools to the real estate industry. She applied her real estate experience in Jeffrey D. Fisher Ph.D. is a Professor Emeritus of Real Estate at the this role to improve and develop UI/UX for to ensure the platform met Indiana University Kelley School of Business, and a Visiting Profes- the research needs of the industry and support clients with incorpo- sor at Johns Hopkins University. He is the Research and Education ration of the platform into their existing research processes. She has Consultant to the National Council of Real Estate Investment Fidu- also served as the Director of Research for the National Council of ciaries (NCREIF) and President of the Homer Hoyt Institute. He is Real Estate Investment Fiduciaries (NCREIF), managing all research a member of the advisory committee to the Real Estate Finance and activities for the private real estate investment management industry Economics Institute at Ecole hôtelière de Lausanne in Switzerland. association. This work included industry education on quarterly data Professor Fisher is a coauthor of Real Estate, 9th edition published product releases via a live webinar presentation, in-house reporting, and by John Wiley and Sons, coauthor of Real Estate Finance and Invest- published articles for Institutional Real Estate Investor. Other previous ments, 14th edition, published by McGraw-Hill, and coauthor of roles include serving as CBRE’s Director of Research and Analysis Income Property Valuation, published by Dearborn. His books have for Texas, and eight years in North American investment research at been translated into Japanese, Korean, and Chinese. Dr. Fisher has Invesco Real Estate. Ms. Rutledge has also taught research methods for published numerous articles in journals such as The Journal of the the Institute of Applied Economics at the University of North Texas. American Real Estate and Urban Economics Association, Journal Ms. Rutledge serves on the ULI Chicago Women’s Leadership Initia- of Real Estate Finance and Economics, The Journal of Urban Eco- tive (WLI) Advisory Board and is active in the ULI Research Forum nomics, The Journal of Real Estate Research, Journal of Portfolio and national WLI initiatives. She previously served on the Real Estate Management, National Tax Journal, Public Finance Quarterly, The Research Institute Advisory Board and National Association for Busi- Appraisal Journal, Real Estate Review, The Real Estate Appraiser and Analyst, Real Estate Issues, The New Corporate Finance, and the ness Economics (NABE) Board of Directors. For which she remains Journal of Property Tax Management. Education: Ph.D., Real Estate, an active member and committee volunteer. Education: M.S., Applied Ohio State University; MBA, Wright State University; B.S., Industrial Economics, University of North Texas; BBA, minor study in Economics Management, Purdue University. and Mathematics, University of North Texas.

Journal

Business EconomicsSpringer Journals

Published: Mar 22, 2021

Keywords: Real estate; Investment; Property risk; Hurricane; Climate change

References