Home Sales Research Articles

The effect of capital gains taxation on home sales: Evidence from the Taxpayer Relief Act of 1997

The Taxpayer Relief Act of 1997 (TRA97) significantly changed the tax treatment of housing capital gains in the United States. Before 1997, homeowners were subject to capital gains taxation when they sold their houses unless they purchased replacement homes of equal or greater value. Since 1997, homeowners can exclude capital gains of $500,000 (or $250,000 for single filers) when they sell their houses. Such dramatic changes provide a good opportunity to study the lock-in effect of capital gains taxation on home sales. Using 1982–2008 transaction data on single-family houses in 16 affluent towns within the Boston metropolitan area, I find that TRA97 reversed the lock-in effect of capital gains taxes on houses with low and moderate capital gains. Specifically, the semiannual sales rate of houses with positive gains up to $500,000 increased by 0.40–0.62 percentage points after TRA97, representing a 19–24% increase from the pre-TRA97 baseline sales rate. In contrast, I do not find TRA97 to have a significant effect on houses with gains above $500,000. Moreover, the short-term effect of TRA97 is much larger than the long-term effect, suggesting that many previously locked-in homeowners took advantage of the exclusions immediately after TRA97. In addition, I exploit the 2001 and 2003 legislative changes in the capital gains tax rate to estimate the tax elasticity of home sales during the post-TRA97 period. The estimation results suggest that a $10,000 increase in capital gains taxes reduces the semiannual home sales rate by about 0.1–0.2 percentage points, or 6–13% from the post-TRA97 average sales rate.

Securitization and Asset Price Cycle: Causality and Post-Crisis Policy Reform

This study aims to explain causal relationships between the two dramatic cycles observed in the U.S. during the recent financial crisis - the mortgage credit cycle and the home price cycle. In the viewpoint of mortgage lending, the main demand-side driver for the observed credit concentration from the early 2000s was the extremely low, even negative, user cost of capital (UCC) for owning between 2002-2006. UCC was, in turn, influenced by three key factors - the record low mortgage interest rate, the practical removal of capital gains taxes for home sales, and high expected home price appreciation on the part of home owners. Easing borrowing constraints, gradually from the early 1980s and abruptly from the early 2000s, is also shown as a significant shift factor in the demand side. In the supply-side, the main drivers for the boom-bust of mortgage credit between early to mid 2000s are identified to be: the favorable interest rate environment, the low rates as well as the sizable yield spreads, which rapidly changed from 2005; the generous bond ratings at initial security issuance, followed by abrupt and deep post-issuance downgrading from 2007; and, the sudden rise of the private-label (PL) RMBS issuers from 2004, resulting in gradual deterioration of the mortgage underwriting criteria. Preliminary econometric analyses show that: the share of PL RMBS in the total issuance Granger-caused the home price growth, but not the other way around; and, the home price boom-bust in the 2000s was significantly influenced by UCC and the share of PL RMBS, but not by the total issuance volume of RMBS. Policy implications of the findings are discussed, in particular, regarding micro- and macro-prudence regulations governing the mortgage-MBS sector.

Predicting consumer behavior with Web search

Recent work has demonstrated that Web search volume can "predict the present," meaning that it can be used to accurately track outcomes such as unemployment levels, auto and home sales, and disease prevalence in near real time. Here we show that what consumers are searching for online can also predict their collective future behavior days or even weeks in advance. Specifically we use search query volume to forecast the opening weekend box-office revenue for feature films, first-month sales of video games, and the rank of songs on the Billboard Hot 100 chart, finding in all cases that search counts are highly predictive of future outcomes. We also find that search counts generally boost the performance of baseline models fit on other publicly available data, where the boost varies from modest to dramatic, depending on the application in question. Finally, we reexamine previous work on tracking flu trends and show that, perhaps surprisingly, the utility of search data relative to a simple autoregressive model is modest. We conclude that in the absence of other data sources, or where small improvements in predictive performance are material, search queries provide a useful guide to the near future.

Predicting the Present with Google Trends

Can Google queries help predict economic activity?The answer depends on what you mean by predict. Google Trends and Google Insights for Search provide a real time report on query volume, while economic data is typically released several days after the close of the month. Given this time lag, it is not implausible that Google queries in a category like Automotive/Vehicle Shopping during the first few weeks of March may help predict what actual March automotive sales will be like when the official data is released halfway through April.That famous economist Yogi Berra once said It's tough to make predictions, especially about the future. This inspired our approach: let us lower the bar and just try to predict the present. Our work to date is summarized in a paper called Predicting the Present with Google Trends. We find that Google Trends data can help improve forecasts of the current level of activity for a number of different economic time series, including automobile sales, home sales, retail sales, and travel behavior. Even predicting the present is useful, since it may help identify turning points in economic time series. If people start doing significantly more searches for Real Estate Agents in a certain location, it is tempting to think that house sales might increase in that area in the near future.Our paper outlines one approach to short-term economic prediction, but we expect that there are several other interesting ideas out there. So we suggest that forecasting wannabes download some Google Trends data and try to relate it to other economic time series. If you find an interesting pattern, post your findings on a website and send a link to econ-forecast@google.com. We'll report on the most interesting results in a later blog post.It has been said that if you put a million monkeys in front of a million computers, you would eventually produce an accurate economic forecast. Let's see how well that theory works.

Hedonic analysis with locally weighted regression: An application to the shadow cost of housing regulation in Southern California

This paper investigates the role of hedonic model misspecification through inappropriate geographic aggregation in the debate over the effects of housing regulation. We use locally weighted regression (LWR) techniques and geo-referenced data to allow the housing hedonic parameters to vary over space. This modeling strategy better represents micro-market realities and the importance of location as a prime determinant of housing prices. Our results, based on a unique dataset of almost 14,000 single-family home sales between 1993 and 2001 in Southern California, suggest regulation had strong direct impacts on the housing market as suggested by Glaeser and Gyourko (2003) and Cheung et al. (2009a) and not indirectly through increased land scarcity as suggested by Davis and Palumbo (2007).

An analysis of the neighborhood impacts of a mortgage assistance program: A spatial hedonic model

Down payment or closing cost assistance is an effective program in addressing the wealth constraints of low-and moderate-income homebuyers. However, the spillover effect of such programs on the neighborhood is unknown. This paper estimates the impact of the City of Dallas Mortgage Assistance Program (MAP) on nearby home values using a hedonic model of home sales from 1990 to 2006. We define neighborhoods of 1,000 feet around each sale and estimate the average differences in sales prices between neighborhoods with various numbers of MAP properties before and after their appearance. We find that MAP properties tend to locate in neighborhoods with lower property values; however, unless a concentration of MAP properties forms, the infusion of MAP properties has little detrimental impact on neighboring property values. Moreover, low concentration of MAP properties has a modest positive impact on surrounding property values. © 2010 by the Association for Public Policy Analysis and Management.

Valuing open space in a residential sorting model of the Twin Cities

We present an analysis of how open space amenities affect residential location choices using a horizontal sorting model to estimate household preferences for open space. To parameterize the model, a new and rich dataset spanning 17 years of home sales in the seven-county Twin Cities area of Minnesota was assembled. Heterogeneity across types of open space and across households is shown to be a critical determinant of the welfare impacts of open space conservation. Our general equilibrium simulations allow housing prices to re-equilibrate in response to policy-induced demand shifts, demonstrating that as the scale of intervention becomes larger, general equilibrium and partial equilibrium welfare measures increasingly diverge. Finally, we find that the spatial structure of policy is an important component of policy design, suggesting open space policy should be place-specific in its design and implementation.

The Value of Land Use Patterns and Preservation Policies

Abstract In many communities, land-use and open-space preservation have become important concerns. Using a large dataset containing every home sale in the state of Massachusetts over 8 years, this paper simultaneously measures the impact of the mix of land-uses in the immediate neighborhood of a home on property values and the impact of a locally-implemented program, the Community Preservation Act (CPA), which provides funds for local open-space and historic preservation as well as affordable housing. I exploit the panel nature of the dataset to conduct a difference-in-differences analysis using local as well as house level fixed effects to overcome omitted variables bias. My results indicate that, on average, passage of the CPA reduces property values by about 1.5% in Massachusetts towns. However, when I allow the CPA effect to differ by county, I find some heterogeneity -- it increases property values in some communities and reduces them in others. Variation in local spending priorities appears to have little impact on property values or the effect of the CPA. Finally, I find that cropland and pasture, as well as low-density residential development, are the most preferred local land-uses, and that homes are more expensive as one increases distance to highways and active rail lines.

The Impact of Commissions on Home Sales in Greater Boston

Buying or selling a residential property is one of the most important financial decisions for a large majority of households in the United States. In 2007, 68 percent of households owned their own home, more than a third of national wealth was held in residential real estate, and there were 6.4 million sales of existing homes according to estimates from the Department of Housing and Urban Development. Surveys indicate that an overwhelming majority of homes are sold with the aid of a licensed real estate agent or broker. According to a report issued in 1983 in the wake of the Federal Trade Commission’s investigation of the real estate brokerage industry, brokers accounted for the sale of 81 percent of single family homes. The National Association of Realtors (NAR), the largest professional organization of real estate agents in the United States that represents more than half of all licensed agents, estimates that nationally almost 80 percent of residential real estate transactions involve a Realtor. For owners selling their homes this way, real estate agents usually bear a large fraction of the costs of marketing a home: advertising the home, conducting open houses, taking potential buyers on visits to the home, and negotiating offers. Households interested in buying a home often solicit the help of an agent to make appointments to visit properties and arrange financing. In exchange for their efforts, agents are usually compensated with a fixed percentage commission of the sales price, split between the buyer’s agent and the seller’s agent (National Association of Realtors 2007, Chang-Tai Hsieh and Enrico Moretti 2003). If the agents are working for

Dimensioning the Housing Crisis

With the apparent stabilization of home prices and the increase in existing home sales, many investors believe that the housing market has bottomed and is beginning to recover. I believe that such optimism is premature. To be sure, there are many positives in the housing market: Prices have fallen significantly, housing is more affordable now than at any time in the past two decades, and the tax credit for first-time homebuyers has helped spur purchasing. Investors, however, are overlooking two critical factors: (1) the size of the "housing overhang" (i.e., the number of loans in delinquency or foreclosure) and (2) the borrowers with negative equity who are likely to default. Loans continue to move into the delinquency/ foreclosure pipeline at a rapid rate but are moving out at a very slow pace, which creates considerable pressure on the housing market. I estimate the housing overhang to be more than 7 million units?these loans are likely to be liquidated and are creating a huge shadow inventory. Adding bor rowers with substantial negative equity but who have not yet become delinquent, I place the total size of the problem at 11 million to 12 million units; in other words, at the current trajectory, more than one in every five borrowers could face foreclosure if stronger policy measures are not taken. Clearly, the biggest problem for these borrowers is negative equity. A successful modification program must address this issue, which means an increased use of principal forgiveness.

The value of urban tree cover: A hedonic property price model in Ramsey and Dakota Counties, Minnesota, USA

Urban tree cover benefits communities. These benefits' economic values, however, are poorly recognized and often ignored by landowners and planners. We use hedonic property price modeling to estimate urban tree cover's value in Dakota and Ramsey Counties, MN, USA, predicting housing value as a function of structural, neighborhood, and environmental variables, including tree cover, using a spatial simultaneous autoregressive (SAR) error model. We measure tree cover as percent tree cover on parcels, and within 100, 250, 500, 750, and 1000 m. Results show that tree cover within 100 and 250 m is positive and statistically significant. A 10% increase in tree cover within 100 m increases average home sale price by $1371 (0.48%) and within 250 m increases sale price by $836 (0.29%). In a model including both linear and squared tree cover terms, tree cover within 100 and 250 m increases sale price to 40–60% tree cover. Beyond this point increased tree cover contributes to lower price. Tree cover beyond 250 m did not contribute significantly to sale price. These results suggest significant positive effects for neighborhood tree cover, for instance, for the shading and aesthetic quality of tree-lined streets, indicating that tree cover provides positive neighborhood externalities.

Violent Crime, Residential Instability and Mobility: Does the Relationship Differ in Minority Neighborhoods?

This study examines the reciprocal relationship between violent crime and residential stability in neighborhoods. We test whether the form of stability matters by comparing two different measures of stability: a traditional index of residential stability and a novel approach focusing specifically on the stability of homeowners. We also examine whether the racial/ethnic composition of the neighborhood in which this stability occurs affects the instability—violent crime relationship. To test the simultaneous relationship between residential mobility and crime we estimate a dual multivariate latent curve model of the change in the violent crime rate and the change in the rate of home sales while controlling for neighborhood socioeconomic and demographic characteristics using data from Los Angeles between 1992 and 1997. Results indicate that the initial level of violent crime increases the trajectory of residential instability in subsequent years, whether the instability is measured as homeowner turnover specifically, or based on an index of all residents. However, the effect of instability on violent crime is only apparent when measuring instability based on an index of general residential turnover and not when including the presence of owners in this measure, or when measuring it based on homeowner turnover. We consistently find that stable highly Latino communities exhibit a protective effect against violence.

Could We Have Predicted the Recent Downturn in Home Sales in the Four U.S. Census Regions?

This paper analyzes the ability of various models to forecast home sales in the four Censusregions in the United States. We also use the models to predict the downturn in homesales in these regions over the 2004:Q4 to 2009:Q2 period. The findings reveal that,barring the South, there always exists a Bayesian model, which tends to outperform allother models in forecasting home sales over the out-of-sample horizon. In addition, whenwe expose our classical and 'optimal' Bayesian forecast models to predicting the peaksand declines in home sales, we find that barring the South again, the models didreasonably well in predicting the turning point exactly at 2005:Q3 or with a lead.

The Impact of Commissions on Home Sales in Greater Boston

This paper analyzes information on all listed nonrental residential properties within a 15-mile radius of downtown Boston from 1998 to 2007. It shows that a higher commission is associated with a higher likelihood of sale, a modest impact on the days on the market and overall no effect on the sales price.

Planning for Housing Recovery? Lessons Learned From Hurricane Andrew

Problem: Housing recovery is key to revitalizing communities following major natural disasters, and yet there is little empirical research on how housing recovery differs across neighborhoods with different sociodemographic characteristics, what happens to housing markets, or property abandonment after disasters. Purpose: We address these gaps by examining single-family housing recovery, housing sales, and property abandonment following Hurricane Andrew in south Miami-Dade County, FL. Methods: We developed panel models predicting single-family housing recovery to examine the effects of home and neighborhood characteristics and hurricane damage on recovery. We analyzed home sales and properties abandoned to assess the extent and duration of the hurricane impacts and conducted correlation analyses to identify neighborhood attributes associated with post-disaster home sales and abandonment. Results and conclusions: Housing recovery trajectories depended on neighborhood demographic, socioeconomic, and housing characteristics. Rental units and homes in low-income and minority neighborhoods recovered more slowly. Home sales increased significantly, with some properties selling multiple times within a short period especially in heavily damaged nonminority neighborhoods. Property abandonments increased dramatically, potentially creating cascading negative effects in affected neighborhoods. Takeaway for practice: Major natural disasters are likely to be followed by housing market volatility, high rates of property abandonment, and uneven housing recovery. To prevent long-lasting adverse effects, planners should focus on reducing housing turnover, retaining home ownership, and promoting reuse of abandoned properties. State and local governments should consider imposing emergency moratoria on foreclosures and insurance cancelations and providing incentives to encourage the rebuilding of low income and rental properties. Land-bank programs could dampen housing market volatility, and emergency property disposition programs and eminent domain processes could expedite reuse of abandoned properties. However, redevelopment should be consistent with long-term development, equity, and hazard mitigation goals. Research support: This research was supported by funding from the National Science Foundation directly (CMS 0100155) and through the Mid-American Earthquake Center (EEC-9701785). Any opinions, findings, conclusions, or recommendations expressed are those of the authors and do not necessarily reflect the views of the National Science Foundation or the Mid-American Earthquake Center.

Homebuyers and Wildfire Risk: A Colorado Springs Case Study

In recent years, the threat that wildfire poses to homes has received much attention in both the mainstream press and academic literature. However, little is known about how homebuyers consider wildfire risk during the home-purchase process. In the context of a unique wildfire education program, we consider two approaches to examining the relationship between wildfire risk and home purchases. Results from a market-level analysis using home sales price data are compared to household survey results. The household survey validates the market-level analysis and provides further insight into homebuyers and wildfire risk. Specifically, we find that while homebuyers prefer locations near dangerous topography, they also prefer less flammable building materials. However, most homebuyers were unaware of wildfire risk when they made their home-purchase decisions.

Climate Change and Housing Prices: Hedonic Estimates for North American Ski Resorts

We use a hedonic framework to estimate and simulate the impact of global warming on real estate prices at North American ski resorts. To do so, we combine data on resort-area housing values from two sources--data on average values for U.S. Census tracts across a broad swath of the western U.S. and data on individual home sales for four markets in the western U.S. and Canada, each available over multiple decades--with detailed weather data and characteristics of ski resorts in those areas. Our OLS and fixed-effects models of changes in home values with respect to medium-run changes in the share of snowfall in winter precipitation yield precise and consistent estimates of positive snowfall effects on housing values in both data sources. We use our estimates to simulate the impact of likely climate shifts on home values in coming decades and find substantial variation across resort areas based on climatic characteristics such as longitude, elevation, and proximity to the Pacific Ocean. Resorts that are unfavorably located face likely large negative effects on home prices due to warming, unless adaptive measures are able to compensate for the deterioration of conditions in the ski industry.

Nonlinear Spatial and Temporal Effects of Highway Construction on House Prices

This paper studies the effect of a newly completed highway extension on home prices in the surrounding area. We analyze non-linearities in both the effect of distance from the highway and the effect of time relative to the completion of the road segment. While previous studies of the effects of nearby amenities on property and land values have focused on either cross-sectional spatial or temporal patterns, the joint analysis of the two dimensions has not been thoroughly investigated. We use home sale data from a period of 11 years centered around the completion of a new highway extension in metropolitan Los Angeles. We combine a standard hedonic model with a spline regression technique to allow for non-linear variations of the effect along the temporal and spatial dimensions. Our empirical results show that the maximum home price appreciation caused by the new highway extension occurs at moderate distances from the highway after it is completed. Lower price increases for this period are observed for homes sold closer to the highway or much further away. This price pattern gradually fades away in the years following the construction completion. A similar, although weaker, price pattern is also observed in the first years of the construction period. There is no statistically significant distance dependency in the 2 years in our sample prior to the beginning of the construction. This indicates that the housing market is not fully efficient as the information about the impending construction of the highway is not immediately incorporated into sales prices.

Days on Market and Home Sales

In April 2006, the real estate listing service in Massachusetts adopted a new policy that prohibits home sellers from resetting their properties' on through relisting. We study the effect of this new policy on home sales along the Massachusetts-Rhode Island border, using homes in Rhode Island, which did not change its relisting policy, as the control group. We find that Massachusetts homes that were on the market at the time of the policy change suffered an average reduction of $16,000 in sale price relative to their Rhode Island counterparts. Homes that were revealed to be slow-moving suffered a greater reduction, but fresher listings only had a small increase in sale price. One reason is that some buyers were unaware of sellers' manipulation of days on market and were thus unable to recognize home listings that were authentically fresh. A direct homeowner survey confirmed that buyer awareness was indeed lacking. Sellers reacted to the new policy by lowering their initial listing price to sell fast. However, in towns where listing price history was more transparent, sellers set a higher listing price to dampen the negative signal of slow sales.

Predicting turning points in the housing market

We identify leading indicators of changes in the housing market and compare their performance in predicting turning points. Being able to predict turning points is of importance to the home building industry, homeowners, and makers of housing policy. Our leading indicators include the Wells Fargo/NAHB Housing Market Index, two of its forward looking components, and an index of consumer sentiment regarding purchasing a home. Our comparison tests include Granger causality and a Bayesian predictor of the probability of a turning point. We find that the measure of consumer sentiment performs relatively well compared to the HMI in predicting home permits, housing starts, and new home sales.