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Planning for Housing Recovery? Lessons Learned From Hurricane AndrewYang Zhang a; Walter Gillis Peacock bcdef

a Urban Affairs and Planning Program, Virginia Polytechnic Institute and State University, b

Department of Landscape Architecture and Urban Planning, Texas A&M University, c SustainableCoastal Margins Program, Texas A&M University, d Hazard Reduction and Recovery Center, TexasA&M University, e College of Architecture, Texas A&M University., f Rodney L. Dockery EndowedProfessor in Housing and the Homeless, Texas A&M University,

First published on: 12 November 2009

To cite this Article Zhang, Yang and Peacock, Walter Gillis(2010) 'Planning for Housing Recovery? Lessons Learned FromHurricane Andrew', Journal of the American Planning Association, 76: 1, 5 — 24, First published on: 12 November 2009(iFirst)To link to this Article: DOI: 10.1080/01944360903294556URL: http://dx.doi.org/10.1080/01944360903294556

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5

Problem: Housing recovery is key torevitalizing communities following majornatural disasters, and yet there is littleempirical research on how housing recoverydiffers across neighborhoods with differentsociodemographic characteristics, whathappens to housing markets, or propertyabandonment after disasters.

Purpose: We address these gaps byexamining single-family housing recovery,housing sales, and property abandonmentfollowing Hurricane Andrew in southMiami-Dade County, FL.

Methods: We developed panel modelspredicting single-family housing recovery toexamine the effects of home and neighbor-hood characteristics and hurricane damageon recovery. We analyzed home sales andproperties abandoned to assess the extentand duration of the hurricane impacts andconducted correlation analyses to identifyneighborhood attributes associated withpost-disaster home sales and abandonment.

Results and conclusions: Housingrecovery trajectories depended on neigh-borhood demographic, socioeconomic, andhousing characteristics. Rental units andhomes in low-income and minority neigh-borhoods recovered more slowly. Home salesincreased significantly, with some propertiesselling multiple times within a short periodespecially in heavily damaged nonminorityneighborhoods. Property abandonmentsincreased dramatically, potentially creatingcascading negative effects in affectedneighborhoods.

Takeaway for practice: Major naturaldisasters are likely to be followed by housingmarket volatility, high rates of property

Planning for HousingRecovery?

Lessons Learned From Hurricane Andrew

Yang Zhang and Walter Gillis Peacock

Following a devastating natural disaster, restoring housing is one of themost important aspects of community recovery. Housing is not onlythe shelter and primary investment of most residents, it is also a critical

component of the local economy and social fabric (Campanella, 2006; Comerio,1998). Disaster recovery is a complicated process that often manifests thetension between speed and deliberation (Olshansky, 2006; Olshansky, Johnson,Horne, & Nee, 2008; Peacock with Ragsdale, 1997). Researchers have longadvocated planning ahead for post-disaster reconstruction and recovery (Berke,

abandonment, and uneven housing recovery.To prevent long-lasting adverse effects,planners should focus on reducing housingturnover, retaining home ownership, andpromoting reuse of abandoned properties.State and local governments should considerimposing emergency moratoria on fore-closures and insurance cancelations and pro-viding incentives to encourage the rebuildingof low income and rental properties. Land-bank programs could dampen housingmarket volatility, and emergency propertydisposition programs and eminent domainprocesses could expedite reuse of abandonedproperties. However, redevelopment shouldbe consistent with long-term development,equity, and hazard mitigation goals.

Keywords: housing, recovery planning,property abandonment, disaster planning

Research support: This research wassupported by funding from the NationalScience Foundation directly (CMS 0100155)and through the Mid-American EarthquakeCenter (EEC-9701785). Any opinions,findings, conclusions, or recommendationsexpressed are those of the authors and do notnecessarily reflect the views of the National

Science Foundation or the Mid-AmericanEarthquake Center.

About the authors:Yang Zhang ([email protected]) is an assistantprofessor in the Urban Affairs and PlanningProgram at the Virginia Polytechnic Instituteand State University. His research focuseson environmental planning, sustainablecommunities, natural hazards planning,hazards mitigation, and recovery. WalterGillis Peacock ([email protected]) is aprofessor in the Department of LandscapeArchitecture and Urban Planning and theSustainable Coastal Margins Program, direc-tor of the Hazard Reduction and RecoveryCenter, interim executive associate dean ofthe College of Architecture, and the RodneyL. Dockery Endowed Professor in Housingand the Homeless at Texas A&M University.His research focuses on long-term housingrecovery, hazard mitigation, and communityresiliency.

Journal of the American Planning Association,

Vol. 76, No. 1, Winter 2010

DOI 10.1080/01944360903294556

© American Planning Association, Chicago, IL.

76-1 429629 Zhang qc2:JAPA 70-1-8 Laurian 12/10/09 4:45 PM Page 5Downloaded By: [Zhang, Yang][Virginia Tech University Libraries] At: 16:42 14 January 2010

Kartez, & Wenger, 1993; Geis, 1996; Haas, Kates, &Bowden, 1977; Kartez & Lindell, 1987; Schwab, Topping,Eadie, Deyle, & Smith, 1998; Wilson, 1991) since deliber-ations after the impact are difficult and often limited bypressure to rebuild quickly (Olshansky, 2006; Olshansky etal., 2008). Lessons from past disasters can help plannersanticipate the problems that are likely to emerge when thenext disaster strikes and to design policies to address them.

Despite its importance, housing recovery remainsunderstudied (National Research Council, 2006). Thereis little consistent, detailed information about the housingrecovery process, post-disaster housing sales, or changesin residential land use after major natural disasters. Thisarticle seeks to fill these gaps in the literature by examiningthree topics: housing recovery patterns for rental and owner-occupied, single-family structures across neighborhoods withvarying socioeconomic and demographic characteristics;post-hurricane home sales; and property abandonment andland use change in the impact area.

Post-Disaster Housing Recoveryin the United States

Housing recovery processes take different forms inter-nationally, but in the United States, the repair, rebuilding,or replacement of permanent housing after natural disastersis primarily driven by the market (F. L. Bates & Peacock,1987; Bolin, 1985; Bolin & Bolton, 1983; Comerio, 1998;Quarantelli, 1982). Property owners have the responsibilityto repair and rebuild, either by doing the work themselvesor by contracting with others to do the work. Governmentdoes not take an active role in housing recovery, but focusesmainly on emergency response and providing short-termaccommodations for displaced victims. The primary fundsfor housing recovery come from private sources, whichinclude personal savings, loans, and insurance, with thelatter being the most important (Comerio, 1998; Peacock,Dash, & Zhang, 2006; Wu & Lindell, 2004). At best,public programs are designed to fill gaps in private resources(Comerio, 1998).

Federal agencies that play roles in housing recoveryfollowing a declared disaster include the Federal EmergencyManagement Agency (FEMA), the Small Business Adminis-tration (SBA), and the Department of Housing and UrbanDevelopment (HUD). FEMA’s Minimal Home RepairProgram (MHR) provides small grants, usually up to$5,000, for minimal repairs required to provide safe andhabitable dwellings. Eligibility under MHR is limited toproperty owners who have insufficient insurance settlementsto cover repair and rebuilding costs. SBA provides housing

recovery assistance through its Disaster Loan Programdesigned to cover rebuilding costs that were not insured orwere underinsured. Because this is a subsidized loan programrather than a grant, SBA has discretion to determine loanamounts, interest rates, and terms based on the borrower’scredit and the value of the property to be repaired. In recentyears, HUD has begun to play a greater role in housingreconstruction through its Community Development BlockProgram (CDBG) and HOME investment partnershipprogram. These programs provide grants that can be usedto rebuild homes, especially in low- or moderate-incomeareas. Localities in federally declared disaster areas canapply for these grants or can request that their pendingapplications be expedited.

Some states establish emergency programs that providefunding for housing recovery after major disasters. Califor-nia, for example, created the California Disaster AssistanceProgram (CAL-DAP) following the Loma Prieta earthquake.This program was designed to be a lender of last resort(Comerio, 1998) for homeowners who had unmet housingreconstruction needs after receiving insurance payouts andgrants or loans from federal programs. Some communitieshave worked successfully with the Red Cross and othercharitable organizations to channel donations towardrebuilding private housing on an ad hoc basis (Eadie, 1998).Additional programs developed in the aftermath of hurri-cane Katrina, such as the Road Home Program and theSmall Rental Property Owners Program have had onlymixed success thus far (L. Bates & Green, 2008).

Research has identified income and race and ethnicityas determinants of normal housing attainment (Alba &Logan, 1992; Bratt, Hartman, & Meyerson, 1986; Flippen,2004; Horton, 1992; Lake, 1980; Oliver & Shapiro, 1995).It has been suggested that these factors can be expected totake on added significance in housing recovery given themarket-driven approach to recovery in the United States(Bolin, 1982, 1985; Bolin & Stanford, 1991, 1998b; Cutter,Boruff, & Shirley, 2003; Haas et al., 1977; Oliver-Smith,1990, 1991). Minorities and low-income households arelikely to have insufficient funds to rebuild and repair theirhousing (Dash, Peacock, & Morrow, 1997; Kamel &Loukaitou-Sideris, 2004; Levine, Esnard, & Sapat, 2007;Peacock & Girard, 1997; Rubin, 1985). Thus, the literaturepredicts low-income and minority neighborhoods will beslower to rebuild housing than others.

Deciding to rebuild a rental property is different thandeciding to rebuild an owner-occupied unit. For mosthomeowners, in addition to being primary shelter, the homeis their biggest investment. Thus, most owner occupantsrebuild their damaged properties as fast as possible (Comerio,1998). For rental properties, by contrast, the owner’s

6 Journal of the American Planning Association, Winter 2010, Vol. 76, No. 1


decision to repair or rebuild is primarily based on long-termprofitability (Comerio, 1998). In research after the WhittierNarrows, Loma Prieta, and Northridge earthquakes, Bolin(1986) and Comerio, Landis, and Rofe (1994) noticed thatsome landlords delayed repairs or abandoned damagedproperties because they had limited finances and concernsabout the future returns on these investments. This line ofresearch suggests that rental properties may take longer torepair and rebuild.

There has been no research on housing sales followingmajor disasters. It is conceivable that sudden damage to thehousing stock can alter decisions to buy or sell properties.Limited anecdotal evidence suggests that housing salesincreased in the impact areas following Hurricanes Andrewand Katrina, but no systematic studies have appeared inthe literature. Home sales after a disaster might occurbecause homeowners decided to permanently relocate toother areas. Indeed, a natural disaster may reinforce pre-disaster trends as some victims act on previous thoughts ofrelocating. For example, one demographer speculated, andit was partially confirmed, that Anglo households1 woulduse Hurricane Andrew as an opportunity to move out ofHispanic areas and into Anglo communities in countiesnorth of Miami-Dade County (Girard & Peacock, 1997).Households may also sell their homes because they lackthe financial resources to repair or reconstruct them. Thisincrease in supply of homes for sale may cause propertyvalues to plunge, allowing investors, speculators, and de-velopers to pick up properties at bargain prices. Regardlessof the reasons, owners who sell properties without repairingthem delay reinvestment and slow the rebuilding process,potentially threatening long-run neighborhood stability.

Property abandonment and changes in residential landuse are also important aspects of housing and neighborhoodrecovery that have not been investigated. Property abandon-ment has emerged as a chronic problem facing many U.S.cities, especially in areas experiencing economic downturns(Accordino & Johnson, 2000; Dewar, 2006). The aban-donment of properties can bring unwanted consequencesto the community, threatening housing quality and neigh-borhood vitality, harming local businesses and the economy,and lowering the overall quality of life (Accordino &Johnson, 2000; Greenberg, Popper, & West, 1990; Spell-man, 1993). A major natural disaster may trigger housingabandonment if property owners decide to avoid theextreme hardship sometimes associated with restoring adevastated area, or if the disaster wiped out their employ-ment (Dahlhamer & Tierney, 1998; Kroll, Landis, Shen,& Stryker, 1990) or if they lack the resources to repair orrebuild. Understanding abandonment patterns after amajor natural disaster will allow planners to address the

adverse consequences and stimulate reuse that will promotelong-term development and mitigate future hazards.

Research Questions

The extant housing recovery literature, while limited,allows hypothesis testing, but because of the paucity ofresearch on post-disaster housing sales, property abandon-ments, and land use changes, we also adopted an exploratoryapproach. First, we test the following hypotheses on housingrecovery after disasters:

H1: Owner-occupied single-family housing will recovermore quickly than rental single-family housing.

H2: Neighborhood income will have a significantlypositive association with single-family housingrecovery.

H3: Neighborhood minority composition (Hispanicand non-Hispanic Black) will have significantlynegative associations with single-family housingrecovery.

Second, we examine single-family home sales, askingwhether they changed following Hurricane Andrew, and, ifso, what factors are associated with these changes. Last, weexamine patterns of property abandonment and residentialland use change and, if these changes occur, what factorsare correlated with them.

Hurricane Andrew and Its Impactsin Miami-Dade County

Hurricane Andrew, a category 5 hurricane, madelandfall on Monday, August 24, 1992, approximately 20miles south of Coral Gables in Miami-Dade County. Thehurricane’s winds left a wide path of damage in southernportions of Miami-Dade County termed South Dade2 atthe time (Figure 1). Total losses were $26.5 billion in 1992dollars, making it the costliest hurricane until surpassed byKatrina in 2005 (Blake, Rappaport, & Landsea, 2007).More than 60,000 housing units were damaged and about47,000 units were severely damaged (Comerio, 1998),leaving more than 180,000 people homeless for someperiod of time. While damage in the 270-square-mile areaof South Dade was uneven, it was more pervasive andsevere in southern sections of South Dade.

Housing reconstruction following the storm wasfunded primarily (93%) by insurance settlements, withsupplemental funding (less than 7%) from SBA’s loan

Zhang and Peacock: Planning for Housing Recovery 7



Figure 1. Hurricane Andrew’s path in Miami-Dade County, FL.


program, FEMA’s MHR program, and HUD’s CDBGHOME program. According to reports from the Coordi-nated Hurricane Andrew Recovery Team (CHART, 1994),homeowners in Florida received almost $11 billion ininsurance settlements for housing reconstruction. SBAprovided about $400 million in low-interest loans to18,000 homeowners and FEMA’s MHR program suppliedabout $200 million for home repairs. HUD’s CDBG Homeprogram provided about $200 million to the impacted areaas well.

Research Design and Methods

Natural disasters can be conceptualized as abruptinterruptions to the ongoing housing accumulation process(F. L. Bates & Peacock, 2008; Friesma, Caporaso, Gold-stein, Linberry, & McClearly, 1979; Peacock, Killian, &Bates, 1987; Wright, Rossi, Wright, & Weber-Burdin,1979). Therefore, a research design for examining housingrecovery should attempt to distinguish changes that wouldhave occurred over time had no disaster occurred, fromthose that actually occurred because of the disaster. However,these comparisons are difficult using a quasi-experimentaldesign since housing characteristics, development trends,and neighborhood and population characteristics varygreatly from one location to another, regardless of a disaster’sintervention.

The broad development and housing context was quitedifferent in South Dade from that in the remainder of thecounty (Dade County Planning Department, 1992; Peacock,Morrow, & Gladwin, 1997; Portes & Stepick, 1993). Atthe time of the storm, South Dade included a vast area ofscattered, often ill-defined, neighborhoods and agriculturalareas, and only two small incorporated rural towns: Home-stead and Florida City. North Dade, however, was charac-terized by continuous, well-defined, older neighborhoods,denser urban development, and included Miami, MiamiBeach, Hialeah, and Coral Gables among its 24 incorpo-rated municipalities. Unlike North Dade, with its majorityHispanic population and highly developed Cuban ethnicenclave (Portes & Bach, 1985), South Dade’s populationwas still 51% Anglo, 18% non-Hispanic Black, and only30% Hispanic. Historically, the Miami metropolitan areahas been one of the most segregated in the United States,and at the time of the hurricane North Dade remained so,with a dissimilarity index of 71 compared to only 52 inSouth Dade.3

In light of these considerable differences, this researchemploys a panel of over 60,000 single-family houses locatedonly in South Dade, including information from both

before and after Hurricane Andrew. The panel includeshouses that were damaged to varying degrees by the hurri-cane as well as units that were unaffected, allowing us toidentify the hurricane’s impact on housing recovery. Inaddition, the panel also allows us to compare housingrecovery in neighborhoods with different socioeconomicand demographic attributes.

DataWe used longitudinal housing tax appraisal data from

the months just prior to Hurricane Andrew in 1992, andfrom four subsequent years (1993–1996), parcel-level countyland use data from 1991 to 1999, housing transaction datafrom 1991 to 1999, and 1990 census data at the blockgroup level from Summary Tape File 3. The tax appraisaldata provided information on appraised residential buildingvalues and housing characteristics. The parcel land use datacontained detailed parcel land use information for eachyear. The housing transaction data provided sales infor-mation between 1991 and 1999. The TIGER/Line dataprovided geographic boundary definition for census blockgroups. We geocoded the tax appraisal data and the housingtransaction data, and merged them with the parcel land usedataset and the census TIGER/Line file in a GIS.

Sample SelectionThis research focused only on single-family structures,

which made up 61.6% of residential structures in SouthDade at the time of the hurricane. Duplexes, multi-familyresidential structures, cluster homes, condominiums,townhouses, and mixed residential structure types havecomplicated ownership and tenure patterns resulting in verydifferent rebuilding and repair decision processes (Comerio,1998; Wu & Lindell, 2004). Another reason to studysingle-family homes is that these buildings are appraisedseparately from the land on which they are built, allowingus to track damage and rebuilding following the hurricanethrough changes in the appraised values of the residentialstructures.

We considered a parcel to be in single-family residentialuse if it was so designated in the County Land Use Code(CLUC) before Hurricane Andrew. We used several rulesto eliminate from our data any such parcels that did notactually include houses. We dropped any structure that didnot have: a pre-disaster value of $5,000 or more, at leastone bedroom, at least one bathroom, interior space of morethan 500 square feet, or at least one floor. In addition, werequired that parcels to be included in the analysis remainin single-family use throughout the entire study period.After removing the unwanted cases, 60,299 single-familyhomes remained in our final panel dataset.4



Measuring Housing RecoveryWe used the appraised value of single-family structures

to measure hurricane impact and housing recovery. Initialtax appraisal notices for 1992 were being delivered at aboutthe time Hurricane Andrew hit in late August. Many home-owners received tax appraisal notices that were far above thevalues of their hurricane-ravaged homes. There was discussionof adjusting appraisals, but reassessing all damaged structuresduring the few months left in 1992 was rejected as impossible.Following the hurricane, the Dade County property ap-praiser’s office undertook inspections of South Dade homesin 1993 to capture damage and for many years thereafter tocapture appreciation due to rebuilding and repair.

For single-family parcels, Miami-Dade County appraisesland and building values separately. The building’s appraisalvalue is based on its characteristics such as size, materials,rooms, integrity, and decks, while the land value capturesmore ephemeral locational attributes associated with neigh-borhood characteristics, desirability, and amenities. Sincethe separate building appraisals and subsequent appraisalsreflected the initial hurricane damage, followed by repairs,rebuilding, and improvements, we employed them to trackdisaster damage and recovery. Unfortunately, the appraisaldata are useful only through 1996, when an amendmentto the Florida constitution capped annual increases toappraisals at 3%. Because tax appraisals are conducted inthe first six months of every year, the building appraisal for1992 actually reflects the home condition 2 to 8 monthsprior to the hurricane, and the 1993 appraisal reflects thehome condition 5 to 10 months after the hurricane. Simi-larly, the values for 1994, 1995, and 1996 reflect homeconditions 17 to 22, 29 to 34, and 41 to 46 months afterthe hurricane, respectively.

Analysis Strategies and Results

Housing RecoveryWe analyzed the housing recovery in two phases. First,

to provide an overall picture of housing damage and re-covery, we compared average appraised building values justprior to the hurricane in 1992 to those after the hurricanethrough 1996. Second, we developed and estimated a setof panel models to assess the effects of the explanatoryvariables on appraised building values from 1992 throughthe impact and post-disaster years (1993–1996).

Table 1 displays average single-family appraised valuesfrom 1992 to 1996 for the entire sample and for houseswith different degrees of hurricane damage. The averageappraised value of all single-family houses in our sample in1992 was $59,470. This fell to $29,506 following thehurricane in 1993, a loss of 50.4%. By 1994, 17–22 monthsafter the hurricane, the average appraised value was slightlyhigher than before the hurricane, suggesting recovery.These values rose again in 1995, and by 1996, the averageappraised value was $68,324, an increase of 14.9% overthe 1992 average.

There were considerable variations depending ondamage level, however. Homes sustaining no damageshowed relatively stable appreciation over the five-yearperiod, increasing from a pre-hurricane average of $78,784to $97,417 by 1996, a 23.5% increase. Very differentpatterns emerged for homes in the three damage categories.All categories with hurricane damage saw declines in theiraverage assessed values in the first year after the stormranging from a relatively modest 6.9% loss for homes withminor damage to a staggering 85.2% loss for homes suffer-


Table 1. Average appraised values of single-family residential buildings before and after Hurricane Andrew, by amount of damage.

Minor damage Moderate damage Extensive damageTotal No damage (<15%) (15–49.9%) (≥50%)

Residentialbuildings 60,299 1,383 12,455 15,086 31,375

Average Change Average Change Average Change Average Change Average Changeappraised from appraised from appraised from appraised from appraised from

Year value 1992 value 1992 value 1992 value 1992 value 1992

1992 $59,470 0.0% $78,794 0.0% $86,612 0.0% $64,118 0.0% $52,608 0.0%1993 $29,506 −50.4% $82,235 4.4% $80,631 −6.9% $45,389 −29.2% $ 7,806 −85.2%1994 $59,638 0.3% $87,148 10.6% $88,965 2.7% $66,340 3.5% $50,671 −3.7%1995 $64,004 7.6% $93,625 19.1% $93,829 8.3% $70,052 9.3% $55,615 5.7%1996 $68,324 14.9% $97,417 23.5% $97,345 12.4% $74,096 15.6% $60,455 14.9%


ing extensive damage. On average, homes in both the minorand moderate damage categories met or slightly exceededtheir average pre-disaster appraised values in 1994, approx-imately two years after the hurricane. However, it was notuntil 1995 that extensively damaged homes exceeded theirpre-disaster averages. While homes suffering no damagehad 1996 values that were 23.5% higher than their 1992values, this number was only 12.4% for homes sufferingminor damage, followed by 15.6% for moderately damagedhomes, and 14.9% for severely damaged homes. Clearly,the values of damaged homes were not where they mighthave been had they suffered no damage from the storm.

These average patterns do not reflect the situation fora sizeable number of houses in each damage category.Although on average, homes with minor damage returnedto their pre-disaster levels two years following HurricaneAndrew, 5,600 (45%) of these homes were still below theirpre-impact levels. This percentage declined in successiveyears, but even by 1996, 2,488 (20%) failed to reach theirpre-storm appraised values. Trends in other damage cate-gories were similar. Indeed, by 1996, 1,834 (16%) ofmoderately damaged homes and 4,062 (13%) of extensivelydamaged homes had not yet returned to their pre-disasterappraised values. Clearly much more is going on here thanis reflected by the average appraised house values.

We analyzed our panel data to provide more insightsinto the housing recovery process by regressing the appraisedbuilding values from 1992 through 1996 on a set of inde-pendent variables in order to model recovery trajectoriesand assess the relative influences of these factors during theimpact and recovery periods. We estimated three models.Model 1 follows:

ln(BldgValue)it =β0 + δ1(1993it ) + δ2(1994it ) +δ3(1995it ) + δ4(1996it ) + β5(ageit ) + β6(bedroomsit ) +β7(bathsit ) + β8(damageit ) + β9(ownit ) + β10(incomeit ) +β11(Hispanicit ) + β12(Blackit ) + νit [1]

where ln(BldgValue)it is the natural log of appraisedbuilding value for each single-family house i, (i =1 through60,299), for each year t , (t =1992 through 1996). With60,299 houses and five points in time, the total sample sizein this analysis is 301,495.5 The variables 1993, 1994,1995, and 1996 are dummy variables indicating data fromthe years 1993 through 1996. Table 2 lists a descriptionof each variable, along with its data source and descriptivestatistics. We chose a natural log transformation of thedependent variable because appraised building value ispositively skewed. The coefficients on the independentvariables are semi-elasticities, which can be roughly inter-preted as the proportional (or percentage, if multiplied by

100) change in appraised value, given a one-unit change inthe independent variable.6 The constant, β0, represents theaverage log building value in 1992 after removing theeffects of damage (i.e., for undamaged houses) and othervariables, while δ1 through δ4 represent the difference inaverage log building values between the base year (1992)and each year from 1993 through 1996. The coefficientsβ5 through β12 capture the partial effects of the independentvariables on appraised values from 1992 through theimpact and recovery period.

To better capture the impacts of hurricane damage andthe other independent variables at specific times during theimpact and recovery period we also estimated Model 2, asfollows:

ln(BldgValue)it = β0 + δ1(1993it ) + δ2(1994it ) +δ3(1995it ) + δ4(1996it ) + β5(ageit ) + β6(bedroomsit ) +β7(bathsit ) + β8(damageit ) + δ9(1994*damageit ) +δ10(1995*damageit ) + δ11(1996*damageit ) +β12(ownit ) + β13(incomeit ) + β14(Hispanicit ) +β15(Blackit ) + νit [2]

and Model 3 as follows:

ln(BldgValue)it = β0 + δ1(1993it ) + δ2(1994it ) +δ3(1995it ) + δ4(1996it ) + β5(ageit ) + β6(bedroomsit ) +β7(bathsit ) + β8(damageit ) + δ5(1994*damageit ) +δ6(1995*damageit ) + δ7(1996*damageit ) + β9(ownit ) +δ8(1993*ownit ) + δ9(1994*ownit ) + δ10(1995*ownit ) +δ11(1996*ownit ) + β10(incomeit ) + δ12(1993*incomeit )+ δ13(1994*incomeit ) + δ14(1995*incomeit ) +δ15(1996*incomeit ) + β11(Hispanicit ) +δ16(1993*Hispanicit ) + δ17(1994*Hispanicit ) +δ18(1995*Hispanicit ) + δ19(1996*Hispanicit ) +β12(Blackit ) + δ20(1993*Blackit ) + δ21(1994*Blackit ) +δ22(1995*Blackit ) + δ23(1996*Blackit ) + νit [3]

Model 2 includes a complement of interaction termsbetween the year dummies and damage, which assesses theeffects of damage from 1993 through 1996. Specificallythe δ9, δ10, and δ11 coefficients estimate the change in thedamage effect over the base effect, β8, in 1994, 1995, and1996, respectively. Model 3 includes an additional com-plement of year interaction terms with the own, income,Hispanic and Black variables that allow the effects of tenurestatus, income, and racial and ethnic composition to alsochange relative to the base year, 1992, through the impact(1993) and recovery years (1994–1996).

We subjected the above models to a number of diag-nostic tests. In light of the number of observations, multi-collinearity was not an issue; however, tests indicated the



presence of heteroskedasticity and serial autocorrelation.The finding of heteroskedasticity is not surprising giventhe inclusion of both individual building and neighborhoodcharacteristics in the model. Serial correlation might alsobe expected, since the 60,299 observations repeat measuresover five points in time. We addressed these problems byusing the GLS estimation procedure with Huber andWhite robust estimators (Eicker, 1967; Huber, 1967;White, 1980). In addition, we estimated models usingalternative approaches, including spatial error and lagmodels7 and mixed multi-level models,8 which did notsubstantially alter the findings.

The results of the three models are presented in Table3. Model 1 accounted for 48.5% of the variance in appraisedvalues of single-family residential buildings. The year dummycoefficients suggest a drop in appraised values in the impactyear and increases in subsequent years, net of the otherfactors. The significant coefficient for the hurricane damage

variable (−0.0153) indicates that every 1% increase indamage resulted in a 1.52% reduction in home valuebetween 1992 and 1996. Tenure status had a significantlypositive coefficient (0.363), indicating that owner-occupiedhomes were valued 43.8% higher than rental housingthrough the period, holding all else constant. Income hada significant positive coefficient (.0043), indicating that forevery thousand-dollar increase in neighborhood medianhousehold income, home value increased by 0.43%. Ap-praised values were lower in neighborhoods where minoritieswere a larger proportion of the population. For every 1%increase in the share of the population Hispanic or Black,appraised values decreased by approximately one-half of1% (−0.0047 and −0.0051, respectively).

Model 2 captures the consequences of hurricanedamage better, allowing its effects to vary from 1993 onby including the year interaction variables. The overall R 2

increases significantly to 63%. In 1993, the first year after


Table 2. Definitions and descriptive statistics for variables used in Models 1, 2, and 3.

Variable name Description Source Mean St. Dev.

Dependent Ln(BldgValue92) Natural log of 1992 appraised value 2 to 8 months Tax appraisal data 10.83 0.63variables before Hurricane Andrew

Ln(BldgValue93) Natural log of 1993 appraised value 5 to 10 months Tax appraisal data 9.16 2.08after Hurricane Andrew




Independent Age Home’s age in 1992 Tax appraisal data 23.97 11.77

Bedrooms Number of bedrooms Tax appraisal data 3.31 0.76

Baths Number of bathrooms (full bath = 1, half bath = 0.5) Tax appraisal data 1.99 0.71

Own Owner occupied = 1, renter occupied = 0 Tax appraisal data 0.89 0.32

Damage Percent of 1992 appraised value lost by 1993 Tax appraisal data 53.86 36.93

Independent Income Median household income (in $ thousands) 1990 census block 46.33 22.20group data

Hispanic Percentage Hispanic 1990 census block 16.66 22.99group data

Black Percentage non-Hispanic Black 1990 census block 26.86 15.64group data

Independentvariables:Individualbuildingattributes

Independentvariables:Neighborhoodattributes



Table 3. Panel models predicting the natural log of the appraised value of single-family residential buildings.

Variable Model 1 Model 2 Model 3

Intercept 9.8919** 9.9095** 10.0241**

Year 1993 −.8405** .7640** 1.1938**1994 .6389** .1980** −.1589**1995 .7910** .1982** −.1649**1996 .8827** .2396** −.0772**

Building characteristics Age −.0189** −.0188** −.0187**Bedrooms .1261** .1271** .1274**Baths .3348** .3365** .3557**

Building hurricane damage Damage −.0153** −.0451** −.0455**Damage*1994 .0380** .0383**Damage*1995 .0408** .0413**Damage*1996 .0417** .0423**

Building tenure status Own .3630** .3280** .0392**Own*1993 .0350**Own*1994 .5375**Own*1995 .4545**Own*1996 .4499**

Neighborhood median income Income .0043** .0045** .0063**Income*1993 −.0039**Income*1994 −.0023**Income*1995 −.0014**Income*1996 −.0016**

Neighborhood % Hispanic Hispanic −.0047** −.0055** −.0040**Hispanic*1993 −.0081**Hispanic*1994 −.0004

Hispanic*1995 .0010**Hispanic*1996 .0003

Neighborhood % Black Black −.0051** −.0047** −.0035**Black*1993 −.0030**Black*1994 −.0003Black*1995 −.0008**Black*1996 −.0018**

N 301,495 301,495 301,495

Group N 60,299 60,299 60,299R 2 within .385 .654 .659R 2 between .602 .602 .604R 2 overall .485 .630 .633

Note: The symbol “*” in a variable name indicates an interaction term.

**p < 0.01 (two-tailed).


the storm, every 1% increase in hurricane damage resultedin a 4.41% drop in building value. The negative effects ofhurricane damage remained in the following years, but thenet effects attenuated. In 1994, two years after the storm,each 1% of damage was associated with a 0.54% loss inappraised value.9 In 1995, the net negative effect fell to−0.25% and by 1996, to −0.15% per 1% of damage. Thediminishing influences of storm damage on appraisedbuilding value from 1994 to 1996 indicate that, as recon-struction took place, damaged homes gradually became lessdifferent from undamaged homes, holding other factorsconstant. Nevertheless, the consequences of damage werestill apparent in 1996.

Model 3 expands on Model 2 by allowing tenure,income, and ethnic and racial composition to vary fromthe base year, 1992, through the impact and recovery years.Including the interactions does contribute significantly10

to the model and increases the R 2. Tenure status variessignificantly through the impact and recovery period. Priorto the hurricane (1992), owner-occupied houses wereappraised about 4% higher than rental houses, holdingother factors constant. In 1993, the significant positiveinteraction (0.035) indicates that owner-occupied houseshad values 7.7% higher than rental housing, suggestingthat they retained slightly more value than rental homesfollowing the hurricane impact. The differentials in 1994through 1996 were more spectacular. In 1994, owner-occupied house values were 78% greater than those of

rental properties, with the gap declining slightly to 64% in1995 and 63% in 1996. Consistent with our first researchhypothesis, these differentials suggest that owner-occupiedhousing recovery trajectories were much steeper than thoseof rental housing. As can clearly be seen in Figure 2, whichshows model-predicted house values in each year while con-trolling other variables,11 rental homes lagged dramaticallybehind owner-occupied homes and failed to reach theirpre-disaster values by 1996.

The effect of neighborhood median household incomefluctuated over the period. Prior to the hurricane, appraisedbuilding value was 0.63% higher for every additionalthousand dollars in neighborhood median householdincome. In 1993, the positive effect of income actuallydropped (−0.0039), yielding a net effect of 0.24% perthousand dollars, suggesting that hurricane damage reduceddifferences in home values across neighborhoods of variedincomes. The negative coefficients for the year-incomeinteraction terms indicate the effect of income on homevalue was lower in 1994 through 1996 than in the base year(1992), but the effects of income did increase significantlyfrom the low in 1993. Specifically, total income effectswere 0.4% per $1,000 in 1994, 0.49% in 1995, and 0.47%in 1996, with the net effects for the last two years beingessentially equivalent. These findings are consistent withour second hypothesis. While hurricane damage reduceddifferences in home values across neighborhoods of varyingincome levels, houses in higher income neighborhoods


Figure 2. Recovery trajectories for owner-occupied and rental single-family home values.

$0

$20,000

$40,000

$60,000

$80,000

$100,000

1992 1993 1994 1995 1996

Owner-occupiedRental


experienced higher gains in the recovery period, suggestingthat they were rebuilt more quickly than was housing inlower-income neighborhoods. However, the 1996 differ-entials were actually lower than those in 1992, suggestingthat the hurricane reduced rather than exacerbated differ-ences in home values among neighborhoods of differentincomes, unlike what some of the literature suggests.

On the whole, with respect to minority neighborhoods,our findings were mixed. The regression revealed slowerrecovery trajectories overall for housing in minority areas,however, the results were different for Hispanic and non-Hispanic Black areas. In 1992, housing values were 0.40%lower for every additional 1% increase in the share of aneighborhood’s population that was Hispanic. In the firstyear after the hurricane (1993), these negative effectsincreased over threefold to −1.2%, suggesting that housingin Hispanic areas experienced disproportionally higherlevels of damage than housing in Anglo areas. By 1994, thenet negative effect had fallen to close to what it had beenprior to the storm, to −0.44% per 1% Hispanic share ofthe population, and it fell still further to −0.30% per 1%share Hispanic in 1995, indicating that negative differentialsfor housing in Hispanic areas diminished in the two yearsfollowing the hurricane. Nevertheless, by 1996, the negativeeffect returned to essentially the same as it had been in1992. Overall these results suggest that housing recoverytrajectories in Hispanic neighborhoods fluctuated a gooddeal. While Hispanic areas experienced higher losses, their

recovery trajectories were quite rapid, temporarily narrow-ing the housing value gap with Anglo neighborhoods, butreturning by 1996 to the same gap as in 1992. Figure 3displays the recovery trajectories for homes in neighborhoodswith different Hispanic population shares, controlling forother variables. Clearly, the gap narrows between 1994 and1995, but widens again thereafter.

The results for neighborhood Black population sharesare somewhat different than those for Hispanics. In 1992,building values were 0.35% lower for each 1% increase inBlack neighborhood population. In the impact year, 1993,this negative effect rose significantly to 0.65% per 1%increase in Black share, again suggesting that losses weredisproportionally higher in neighborhoods with higher pro-portions Black compared to Anglos. In 1994, the negativeeffect attenuated to −0.38%, essentially the same differentialas in 1992. This suggests that initial rebuilding paralleledthat in more heavily Anglo areas. However, in 1995 and1996 the net effects became more negative (−0.43% and−0.53%, respectively). These findings suggest that rebuildingand repairs were happening more slowly in predominatelyBlack neighborhoods and that they fell further behind after1994. Indeed, by 1996 the negative effect on single-familyhome values associated with large Black population shareswas substantially higher than it had been prior to thehurricane in 1992. Figure 4 displays the model-predictedtrajectories for house values in neighborhoods with threedifferent shares of Black population. The gaps grow pro-


Figure 3. Recovery trajectories for home values in neighborhoods with different Hispanic population shares.

$0

$20,000

$40,000

$60,000

$80,000

$100,000

1992 1993 1994 1995 1996

10% Hispanic30% Hispanic60% Hispanic


gressively from 1994 through 1996 as houses in neighbor-hoods with small Black shares reach and surpass 1992 levelsby 1996, while those with large Black shares fail to evenreach pre-disaster levels.

Home SalesWe first examined home sales by considering trends

in the total number of home sales in South Dade over anexpanded nine-year period from 1991, the year before thehurricane, to 1999. We were particularly interested inshifts in sales volume and houses that were sold more thanonce during the recovery period. Figure 5 shows the volumeof single-family homes transactions in South Dade for eachyear from 1991 to 1999. Home sales increased dramaticallyin 1992, the year of Hurricane Andrew. In 1991, beforethe hurricane, 3,992 transactions were recorded, but in1992, the volume soared to 11,729, a 193.8% increase.Interestingly, 9,582 or 81.7% of 1992 transactions occurredfrom September to December, the four months followingthe hurricane. Sales dropped slightly in 1993 to 3,555transactions, picked up significantly in 1994 and 1995with 5,095 and 5,305 transactions, respectively, and finallyleveled off at around 4,200 sales per year from 1996 to1999. Figure 6 reveals that the number of properties sellingmore than once increased dramatically in the hurricaneyear (1992), when 1,147 homes sold twice and 77 homessold three or more times in a single year. Again, most ofthese multiple sales (87.3%) occurred between Septemberand December. This may be an indication of speculators

flipping housing (buying properties at low prices andquickly selling them at higher prices) following the storm.Such multiple sales were substantially higher in 1992 thaneither before the storm or in subsequent years, when theyreturned to pre-hurricane levels.

To examine which factors were related to sales acrossneighborhoods, we computed the proportion of single-familyhouses that were sold during the four months following thehurricane in 1992 (September through December) in eachof South Dade’s 146 block groups and correlated it withaverage pre-disaster home value, average hurricane damage,percentage of owner-occupied housing, median householdincome, and percentage Hispanic and percentage Black inthese block groups. Table 4 shows the correlations, whichindicate that home sales were more likely in areas withhigher damage (r = 0.18) and higher percentages of Anglos(r =.07). Conversely, home sales were less likely in areas withhigher incomes (r = −0.03) and higher percentages Black(r = −0.08). There were no significant correlations betweenhome sales and pre-hurricane home values, percentages ofhomes owner-occupied, or percentages Hispanic. Thesefindings suggest that sales were concentrated in Anglo areasand areas with more damage, but not in minority areas.

Property Abandonment andLand Use Change

We also used a longitudinal approach to analyzeproperty abandonment and residential land use change.We considered a parcel abandoned if its county land use


Figure 4. Recovery trajectories for home values in neighborhoods with different Black population shares.

$0

$20,000

$40,000

$60,000

$80,000

$100,000

1992 1993 1994 1995 1996

10% Black30% Black60% Black


classification changed from single-family residential tovacant. On the other hand, if a residential parcel wasconverted to commercial, industrial, or another nonresi-dential use we considered it a change in land use. Figure 7shows abandonments and land use changes in South Dade

from 1991 to 1999. The year of the hurricane, 1992, sawa major spike in both categories, with 556 abandonmentsand 88 conversions to other uses. Fewer properties wereabandoned in 1993 and 1994, but these levels were stillsubstantially higher than in 1991. Beginning in 1995,


Figure 5. Annual home sales in South Dade, 1990−1999.

1,000

3,000

5,000

7,000

9,000

11,000

13,000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Year

Nu

mb

erof

tran

sact

ion

s

Figure 6. Annual sales of properties selling more than once in a year in South Dade, 1990−1999.

0

200

400

600

800

1,000

1,200

1,400

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Properties sold three or more times

Properties sold more than once

Properties sold three or more times

Properties sold more than once


property abandonments and land use changes stabilized atlevels similar to the pre-hurricane years.

We conducted another correlation analysis betweenthe percentage of single-family parcels that were abandonedin each block group between 1992 and 1994 and the sameset of neighborhood variables employed above. The results(shown in Table 5) revealed that higher levels of abandon-ment occurred in heavily damaged neighborhoods (r = 0.39)

and in lower-income (r = −0.31) and minority, particularlyBlack (r = 0.27), neighborhoods. Over 15% of single-familyproperties were abandoned during this period in some ofthese neighborhoods. On the other hand, we found lowerlevels of abandonment in predominately Anglo neighbor-hoods (r = −0.32,) and neighborhoods with larger shares ofowner-occupied housing. Interestingly, while high levels ofdamage are correlated with both sales and abandonments,


Table 4. Correlations between percentages of homes in each block group sold after the hurricane in 1992 and block group characteristics.

Average% homes sold homeSeptember value Medianthrough Average before % owner householdDecember damage hurricane occupied income % Black % White

Average damage 0.18*Average home value

before hurricane −0.03 −0.29*% owner occupied 0.01 −0.21* 0.26*Median household income −0.03* −0.43* 0.57* 0.36*% Black −0.08* 0.30* −0.33* −0.31* −0.48*% White 0.07* −0.31* 0.37* 0.37* 0.59* −0.81*% Hispanic 0.02 0.02 −0.07* −0.10* −0.20* −0.29* −0.32*

Note: N = 146 census block groups.

*p < .05

Figure 7. Abandoned and converted single-family parcels in South Dade, 1991−1999.

0

100

200

300

400

500

600

700

1991 1992 1993 1994 1995 1996 1997 1998 1999

Abandonments Land use changes


sales are more correlated with higher proportions of thepopulation who were Anglo, while abandonment is morecorrelated with higher proportions minority, particularlyBlack.

Discussion and Conclusions

We addressed three critical gaps in the literature bysystematically examining long-term single-family housingrecovery, housing sales, and property abandonment andresidential land use change following Hurricane Andrew.We found that effects of hurricane damage, which wemeasured as reductions in assessed home values, were longlasting when we looked specifically at homes that weredamaged by the storm. We also found that owner-occupiedhomes recovered much faster than rental homes, as hadbeen theorized previously, but never demonstrated. Althoughhigher income neighborhoods recovered somewhat fasterthan others, the difference was not as striking, perhapsbecause income is not a perfect measure of the resourcesavailable to households to fund recovery. We found thathaving a high proportion of minority residents significantlyshaped a neighborhood’s housing recovery. Housingrecovery in minority neighborhoods was slower than inAnglo neighborhoods, leaving them worse off in the longterm, and Blacks fared particularly poorly. We also foundthat both home sales and residential property abandonmentincreased dramatically following Hurricane Andrew. Al-though we could not explain the reasons for this definitively,

these trends are detrimental to neighborhood stability.These findings are explained in detail as follows.

The Effects of Hurricane Damage WereLong Lasting

Consistent with the literature, we found that overallaverage assessed single-family home values in South Dadereturned to pre-disaster levels within two years (Comerio,1998; Wu & Lindell, 2004). However, a different pictureemerged when we controlled for damage. Homes thatsuffered no damage appreciated by 23.5% from 1992 to1996, while homes damaged by the hurricane on averagefailed to reach levels comparable to undamaged housing by1996.

Using our panel data to estimate regression models, wefound that the damage effect dropped markedly from 1993to 1994 and declined steadily in 1995 and 1996. However,every 1.0% of additional damage still resulted in 0.32%lower appraised values in 1996. Given these findings forsingle-family homes, we disagree with the conclusion basedon aggregated assessment data (Friesma et al. 1979; Wrightet al. 1979) that housing recovery is largely complete withintwo years, with few lingering consequences. Rather, wefound that damaged properties take much longer than twoyears to return to pre-disaster assessed values.

Owner-Occupied Homes Recovered FasterThan Rental Units

One of the most dramatic differences we found inrecovery patterns was the disparity between owner-occupied


Table 5. Correlations between percentage of single-family parcels in each block group abandoned in 1992 through 1994 and block group characteristics.

Average% parcels homeabandoned value Median

in Average before % owner household1992–1994 damage hurricane occupied income % Black % White

Average damage 0.39*Average home value

before hurricane −0.14* −0.29*Percentage owner occupied −0.24* −0.21* 0.26*Median household income −0.31* −0.43* 0.57* 0.36*% Black 0.27* 0.30* −0.33* −0.31* −0.48*% White −0.32* −0.31* 0.37* 0.37* 0.59* −0.81*% Hispanic 0.07* 0.02 −0.07* −0.10* −0.20* −0.29* −0.32*

*p < .05.

Note: N = 146 census block groups.


and rental homes. While owner-occupied homes’ appraisedvalues were only about 4% higher than those of rentedhomes on average prior to the hurricane, their appraisalswere 78% higher than rental properties on average in1994, declining only slightly to be 63% higher on averagein 1996. These extraordinary differentials provide the firstsystematic empirical evidence supporting previous sugges-tions that rental properties take longer to rebuild (Bolin,1986; Comerio et al. 1994). These results are likely relatedto the qualitatively different decisions faced by the owneroccupants of single-family houses compared to investors inrental properties. However, as Comerio (1998) has pointedout, these differentials may also partly reflect recoverypolicies that focus on assisting owner occupants, with veryfew programs addressing rental housing. More research isneeded to understand the factors causing these differences,the recovery of other types of rental properties, and theconsequences of slow recovery rates for rental markets.

Income Not as Important as ExpectedThe literature clearly anticipated that income would

have a positive effect on housing recovery, and some sug-gested that the effect would become more pronounced inthe recovery period than it had been before (Bolin, 1982,1985; Bolin & Stanford, 1991, 1998b; Cutter et al., 2003;Haas et al., 1977; Oliver-Smith, 1990, 1991). However,we found smaller disparities after the hurricane than hadexisted before it. Higher income neighborhoods led theway in the recovery process, but this difference was not asgreat as might have been expected. While we found differ-ences in recovery associated with neighborhood income,we suspect that the underlying differences result at leastin part from whether households had insurance and otherresources for recovery. Thus, future research should aim tocollect data on these factors in order to better understandtheir contributions.

Housing Recovery in MinorityNeighborhoods Was Slower, Leaving ThemRelatively Worse Off in the Long Term

Anglo areas fared far better than minority neighbor-hoods. However, there were considerable differences be-tween Hispanic and non-Hispanic Black areas. Our resultssuggest that areas with high shares of Hispanic residentswere hit hard by the hurricane, made substantial gains inthe next two years to actually narrow the gap with Angloareas by 1995, but fell back by 1996. Non-Hispanic Blackareas were also hit hard, but their gains in 1994 werenowhere near those of Hispanic areas, and they fell pro-gressively further behind after that. Indeed, areas with highconcentrations of Blacks were substantially worse off

relative to Anglo areas in 1996 than they had been prior tothe hurricane.

These findings are generally consistent with the disasterrecovery literature as well as the more general literature onhousing and insurance among minorities. Minority house-holds generally lack high quality property insurance tocover reconstruction (Bolin & Standford, 1998b; Comerio,1998; Peacock & Girard, 1997) and often face differentialaccess to SBA loans because they fail to apply or qualify(Dash et al., 1997; Bolin & Stanford, 1998a, 1998b).Moreover, minority households’ limited economic powerand political representation make them less likely to haveinput into planning for disaster recovery programs andactivities (Bolin & Stanford, 1998b; Morrow & Peacock,1997; Olshansky, 2006; Olshansky et al., 2008; Tierney,1989). This may explain why South Dade’s Hispanicneighborhoods recovered faster than its Black neighborhoodsafter Hurricane Andrew. As noted earlier, Cuban Hispanicshave gained significant economic and political power inthe Miami area in past decades (Portes & Bach, 1985;Grenier & Morrow, 1997). It is likely that if we assessedthe recovery trajectories of Cuban Hispanics, non-CubanHispanics, and non-Hispanic Blacks separately, very differ-ent patterns might emerge. Future research should considerwhether race and ethnicity explain variation in resourcesavailable for housing recovery.

Home Sales Increased Markedly FollowingHurricane Andrew

Home sales became unusually active, especially in thefour months immediately following Hurricane Andrew.During that period, 9,582 transactions were completed,dwarfing the 3,992 sales in the entire year before thehurricane. There were also an unprecedented number ofproperties that were sold multiple times during the period.Sales were higher in neighborhoods with higher levels ofdamage and those with larger Anglo population shares andlower in Black neighborhoods.

These findings substantiate previous anecdotal observa-tions of increased sales following natural disasters, but alsoraise a number of questions. Did they reflect homeownersseizing the opportunity to leave or speculators buyinghomes of those unable to rebuild? The positive correlationsbetween home sales and Anglo neighborhood compositionmay show that the hurricane reinforced preexisting Anglooutmigration from South Dade (Morrow & Peacock, 1997;S. K. Smith, 1996; S. K. Smith & McCarty, 1996). Thenegative correlation between home sales and neighborhoodincome levels and the positive correlation between homesales and damage levels may indicate that some propertyowners were forced to sell their homes because they did



not have the ability to rebuild (Levine et al., 2007; V. K.Smith, Carbone, Pope, Halstrom, & Darden, 2005, 2006).The increased home sales could also be a result of thedeclining employment opportunities in the area followingthe storm. For instance, the permanent closure of theHomestead Air Force Base alone cost its surroundingcommunity about 21,000 jobs. Clearly, more empiricalresearch would allow testing these propositions. Regardless,volatility created by a post-disaster spike in home salescould delay rebuilding at least in the short run. In addition,market instability caused by such housing sell-offs couldinduce subsequent resident flight (Rohe & Stewart, 1996)and start property values and neighborhoods on a downwardtrajectory in the long run.

Property Abandonment IncreasedProperty abandonments in the hurricane year were ten

times those in the year before the hurricane. Five timesmore residential properties changed to other land uses inthe year of the hurricane than had in the previous year, andthis rate remained high for a couple of years. Abandonmentoccurred in heavily damaged, lower income, rental, andminority, especially Black, neighborhoods, some of whichexperienced more than 15% abandonment. While bothsales and abandonments occurred in areas with heavierdamage, abandonments were more concentrated in lowerincome, rental, and minority areas, while sales were moreconcentrated in Anglo areas.

The concentration of abandonments in more vulnerableneighborhoods is alarming, since abandoned propertiesadversely affect housing values, neighborhood stability, andlife quality (Accordino & Johnson, 2000; Greenberg et al.,1990; Spellman, 1993). Immediately after the storm,abandonments may have been the result of property owners’inability to rebuild, but these initial abandonments canhave a cascading effect. As neighborhoods become moreblighted, residents have less desire to stay, and have moredifficulty selling their properties (Massey & Denton,1993). Future research is needed to investigate the causesof post-disaster property abandonments in order to betterunderstand how to reduce them and their consequences.

Limitations of the ResearchLike all research, this study has limitations. First, we

focused exclusively on single-family housing. Other housingtypes, such as duplexes, multi-family rental and condo-minium buildings, and townhouses are also vulnerable tomajor natural disasters. Generalizing to these differentforms of housing may not be appropriate because rebuildingand repairing them would involve substantially differentdecision making processes. We also studied a specific and

limited time period before and after the hurricane, andfound the timing of the annual appraisal cycle less thanoptimal for measuring recovery. Studying a longer timeperiod before and after the storm, and having access toassessments made immediately after the storm and againevery few months thereafter, would make the findings morerobust. Perhaps most importantly, our analysis lacked dataon the income, ethnicity, race, and recovery resources ofthe owners and occupants of the affected properties, relyingon census data for block groups instead. Finally, our resultsdepend in part on Hurricane Andrew’s size and nature.Andrew was principally a devastating wind event, unlikeKatrina, which was a major flooding event. This and theaforementioned limitations suggest caution when general-izing from these results. Nevertheless, our findings do havesome practical implications that may be of use to theplanning community.

Planning for Housing RecoveryFollowing a devastating natural disaster, housing

recovery should not be considered simply a short-termemergency issue nor simply left to the market; it must beregarded as a critical component of a long-term communityrecovery strategy. Major natural disasters are likely to befollowed by higher than usual housing sales and abandon-ments and uneven housing recovery that is systemicallyrelated to neighborhood characteristics and tenure status.It is critical to address these issues with programs andpolicies and to monitor and track sales, abandonment, andrecovery following the event in order to reduce long-lastingadverse effects.

Lack of funding is always a significant barrier to post-disaster recovery (Comerio, 1998; Morrow & Peacock,1997; Olshansky et al., 2008). Thus, it is important tomake the best use of existing housing recovery programsfor both households and municipalities to expedite recon-struction, especially for low income and minority areas.Planners should promote coordination and concertedaction by local, state, and federal recovery programs, aswell as both the for-profit and not-for-profit private sectors,by designating lead agencies such as the local housingagency or planning department. Education and advocacyprograms are critical to help local officials, property owners,and residents, especially those who are members of margin-alized groups, to understand the various housing recoveryprograms and application procedures, as well as their rightsin dealing with insurance companies. It is important toinvest in and build long-term relationships among federal,state, and local agencies involved with recovery and disastermitigation programs. All these actors should make housingpolicy adjustments to reduce the significantly slower rates



of rental housing recovery. Such delays can have significantadverse impacts on community and household recovery inthe long run.

Planners should anticipate and monitor potential vola-tility in housing sales and abandonments following majordisasters. These can signal that property owners are experi-encing difficulties rebuilding and show which neighborhoodsare at risk of cycling downward. Sales and abandonments mayalso represent opportunities to reassess development patterns,perhaps channeling future development away from highhazard areas or toward more appropriate areas.

Planning departments should implement policies inadvance that will support residential property owners duringthe first several months following a disaster. Examples mightinclude state and local emergency moratoria on foreclosuresand insurance cancelations, incentives to repair and rede-velop rental properties, and mortgage assistance for proper-ties in presidentially declared disaster areas. Land-bankingprograms may dampen volatility and facilitate appropriateredevelopment and recovery planning, particularly ifcoupled with incentives for residents to sell their propertiesto the program and provisions that allow former owners toregain their properties or other properties in safer areaswithin a specified time period.

Many state laws have created strict procedures gov-erning how local governments acquire abandoned andvacant properties, either through tax foreclosure or eminentdomain. Typically, localities cannot start these actionsuntil the properties have been delinquent for from two toseven years, and legal processes may take another two yearsto complete (Accordino & Johnson, 2000; Shigley, 2008).Such tortuous procedures invariably delay land reutilizationand the costs associated with prolonged legal proceedingsmay force local governments to give up acquisition proce-dures and leave the abandoned parcels vacant. To expediterecovery following a major disaster, state and local govern-ments should establish emergency procedures to speed upthe acquisition of abandoned parcels in disaster-strickenareas. Of course, planners should ensure that these processespromote renewal and not removal.

As development in hazardous areas continues in theUnited States, the potential for devastating floods, hurri-canes, and other natural disasters is no longer a distantthreat. Communities should proactively plan for recoveryfollowing the next major disaster. Planning for disasterrecovery in general, and housing recovery in particular, isessential to creating sustainable, economically vibrant, andresilient communities. The solutions we prescribe to hous-ing recovery problems demand extensive deliberations andcollaborations among stakeholders. For any community ina disaster-prone area, such deliberations should start today.

AcknowledgmentsWe thank Dr. Nicole Dash, University of North Texas, who madeimportant contributions to the data collection effort. We also thank theeditor and five anonymous reviewers for their insightful comments andsuggestions.

Notes1. Throughout this article, we use the term Anglo or White to refer tothe non-Hispanic non-Black population, Black to refer to the non-Hispanic Black population, and Hispanic to refer to the Hispanicpopulation regardless of race.2. At the time of the hurricane, Miami-Dade County’s official name wasDade County. Areas north of Kendall Drive were generally referred to asNorth Dade and areas south of Kendall Drive were referred to as SouthDade.3. Dissimilarity scores above 60 indicate high levels of racial segregation(Massey & Denton, 1993).4. We dropped 673 single-family parcels based on these rules. Weanalyzed census data for dropped cases and discovered that single-familyresidential parcels without houses tended to be located in areas that hadbelow-average median incomes and in which Whites made up smallerthan average shares of the population.5. Since data are available for all houses at each point in time, this is abalanced panel.6. We computed the technically correct percentage of change as100(eβ − 1) or 100(eδ − 1), and use these values in the followingdiscussion.7. Spatial dependence tests on the full dataset would have required a60,299 by 60,299 weight matrix, exceeding the capacity of eitherSTATA or Geoda statistical software. As a compromise, we conductedthis analysis using a random sample of 1,000 observations from the60,299 cases. We diagnosed spatial autocorrelation in the regressionresiduals using the Moran’s I and Lagrange Multiplier (LM) tests(Anselin, 1988, 2005; Can, 1992). The LM tests showed spatialdependencies present in both autoregressive errors and spatially laggeddependent variables. To discover whether this had affected our estimatedmodel coefficients, we ran estimations for both spatial error models andspatial lag models using three levels of spatial weight matrix (3 miles, 5miles, and inverse distance). The estimated values of coefficients in thesemodels and the models we report here were different, but the magnitudes,signs, and significances remained the same. In other words, these testsdid not alter our substantive conclusions.8. We re-estimated the three models as multilevel, mixed-effects, linearregressions, which produced essentially the same results.9. The net effect is: 100(e(−.0451+.038) − 1) = −0.53669%.10. An LM test was significant: χ2 = 2542.03, p < (.0001).11. The predicted recovery trajectories were constructed by inserting themean values of other independent variables. Since the dependentvariable is the logged home value, the predicted values were calculatedas y = exp(σ 2/2)*exp(log y ) (Wooldridge, 2008), where σ 2 is theunbiased estimator of error variance.

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