Search Benefit in Housing Markets: An Inverted U‐Shaped Price and TOM Relation

The nature of the relationship between a property's selling price and its marketing time in the housing market remains an open question to date, despite almost 40 years of inquiry and hundreds of regressions conducted on various data sources. This study attempts to settle the long‐standing open question by examining the issue from a new perspective. We demonstrate that the true price–TOM relationship should be nonlinear and characterized by an inverted U‐shaped curve wherein the selling price increases with TOM up to a certain threshold, reflective of a positive exposure effect and decreases thereafter to reflect a negative stigma effect. This relationship is borne out in an empirical analysis using a large sample of home sales from the Hampton Roads, Virginia metropolitan area during an extended period of time. We then formulate hypotheses about the benefit of search by home sellers, which are subsequently confirmed by the empirical findings.     

A Model of Time‐on‐Market and Real Estate Price Under Sequential Search with Recall

This article develops a model and provides a closed‐form formula to uncover the theoretical relationship between real estate price and time on market (TOM). Our model shows a nonlinear positive price‐TOM relationship, and it identifies three economic factors that affect the impact of TOM on sale price. We demonstrate that conventional metrics for real estate return and risk, which are borrowed in a naïve fashion from finance theory, do not account for marketing period risk and tend to overestimate real estate returns and underestimate real estate risks. Our model provides a simple way to correct such bias. This theory helps to explain the apparent “risk‐premium puzzle” in real estate.     

Why Do Sellers Hold Out in the Housing Market? An Option‐Based Explanation

In the residential housing market, home owners are reluctant to sell in a declining market. We build a model which focuses on the embedded call option associated with home ownership that allows owners to delay the (irreversible) sale. When prices are low, the (opportunity) cost of a sale, i.e., a higher implied gain from a future sale, likely exceeds its immediate trade benefit and an owner is better off waiting for market conditions to improve. The model also highlights the importance of supply conditions: a more constrained supply is associated with a longer delay. Using state‐level residential housing data, we find evidence consistent with the model. Transaction volume is increasing (decreasing) in the rental growth rate (volatility) in the cross section; their effects are amplified in areas with low supply elasticities, and in times with low market prices. Overall, this paper provides a rational explanation for delayed trading decisions in the housing market.     

Real Estate Returns and Risk with Heterogeneous Investors

This article develops a theoretical framework and formulates a unified risk metric that integrates both real estate price risk and uncertainty of time on market (TOM). We demonstrate that real estate sellers with different degrees of financial distress face not only different marketing period risks, but also receive different return distributions upon successful sales. The major findings of this article can be summarized as follows. First, we show that real estate return and risk, which account for both price and TOM risk, are investor specific, varying over investors with different financial circumstances and holding periods. Second, the traditional valuation of real estate return and risk, which is based solely on the return distribution of a successful sale without considering the uncertainty of TOM and the investor's financial circumstances, underestimates real estate risk and exaggerates real estate return. Third, our empirical applications in both residential and commercial real estate markets show that the Sharpe ratio estimated by the traditional approach is seriously overstated—to the largest extent for investors with high financial distress. In addition, we find that, given the typical 5‐ to 7‐year holding period for real estate, the Sharpe ratios estimated by integrating both price and TOM risk are much in line with the performance of financial assets. These findings can help to explain the apparent “risk‐premium puzzle” in real estate.     

Moral Hazard in Home Equity Conversion

Home equity conversion as presently constituted or proposed usually does not deal well with the problem of moral hazard. Once homeowners know that the risk of poor market performance of their homes is borne by investors, they have an incentive to neglect to take steps to maintain the homes values. They may thus create serious future losses for the investors. A calibrated model for assessing this moral-hazard risk is presented that is suitable for a number of home equity conversion forms: (1) reverse mortgages, (2) home equity insurance, (3) shared-appreciation mortgages, (4) housing partnerships, (5) shared-equity mortgages and (6) sale of remainder interest. Modifications of these forms involving real estate price indexes are proposed that might deal better with the problem of moral hazard.     

A Spatiotemporal Solution for the Simultaneous Sale Price and Time‐on‐the‐Market Problem

There exists an important methodological challenge when dealing with sale price and time‐on‐the‐market variables because both variables are simultaneously determined and related to the motivation of the sellers and buyers. Exploiting the fact that transactions occur over space and time, we propose a two‐stage approach based on instrumental variables (IV) built from information collected from previous transactions. The unidirectional temporal property and the fact that other transactions are exogenous from the perspective of a single buyer or seller are exploited to evaluate the effect of the sale price on time‐on‐the‐market, and the effect of time‐on‐the‐market on the sale price. Based on 29,471 transactions occurring in the suburban neighborhood of Montréal (1992‐2000), the results suggest that, everything else being equal, houses staying longer on the market provide negative information to the market, which results in a lower final sale price, while the final sale price is negatively related to time‐on‐the‐market, indicating that houses of better quality (better amenities) stay less time on the market.     

Institutional Investment,Asset Illiquidity and Post‐Crash Housing Market Dynamics

We examine institutional investors’ entry into the equity side of the single‐family detached housing market using an asset illiquidity framework. We find that institutional investors purchased owner‐occupied houses after the real estate crisis for approximately 6.3–11.8% less than owner‐occupiers. The large discount was in addition to distressed sale and cash purchase discounts which, when combined, highlight the low liquidation value for owner‐occupied housing. The results suggest that asset illiquidity is an important cost of leverage in the owner‐occupied housing market.     

Can Tightness in the Housing Market Help Predict Subsequent Home Price Appreciation? Evidence from the United States and the Netherlands

This article assesses the predictive power of variables that measure market tightness, such as seller's bargaining power and sale probabilities, on future home prices. Theoretical insights from a stylized search‐and‐matching model illustrate that such indicators can be associated with subsequent home price appreciation. The empirical analysis employs listings data on residential units offered for sale through a real estate broker in the Netherlands and for certain U.S. regions. Individual records are used to construct quarterly home price indices, an index that measures seller's bargaining power and (quality‐adjusted) home sale probabilities. Using conventional time‐series models we show that current sale probabilities and bargaining power can significantly reduce home price appreciation forecast errors and help to predict turning points in local area housing markets. The measures and approaches in this article help to demonstrate ways in which researchers and practitioners can leverage listings data to gain knowledge about the current and future state of the housing market.     

Investor Confidence as a Determinant of China's Urban Housing Market Dynamics

China's urban housing market dynamics suggest that evolving investor confidence may be a relevant demand shifter. Such investors are continually updating their beliefs about the state of the macroeconomy and the policy uncertainty related to national and local housing policies. We build a 35 Chinese city real estate confidence index that varies over time and across cities. This index predicts subsequent house price appreciation and new housing sales. We document evidence of heterogeneous effects of investor confidence depending on a city's demographics and the city's elasticity of housing supply. Our results based on a new household‐level expectations survey bolster the case that investor expectations are an important determinant of real estate price dynamics.     

Do House Price Levels Anticipate Subsequent Price Changes within Metropolitan Areas?

This research examines the relationship between hedonically controlled housing price levels and subsequent changes in those prices across locations within MSAs. Are areas with a high price relative to an “imputed rent” paying for higher appreciation? In an efficient market (e.g., Gordon Growth Model), as fundamentals (impute rent) differ across locations and change over time, anticipation of these should generate a positive correlation between (residual) price levels and subsequent price changes. We undertake these tests in four different MSAs using a panel of repeat‐sale house price indices that have been scaled to price levels with the hedonic attributes of the house and ZIP code. In three markets we find that identical houses in higher priced ZIP codes subsequently appreciate faster. In one market we find that there is little statistical difference.     

Risk and Return in the U.S. Housing Market: A Cross-Sectional Asset-Pricing Approach

This article carries out an asset-pricing analysis of the U.S. metropolitan housing market. We use ZIP code–level housing data to study the cross-sectional role of volatility, price level, stock market risk and idiosyncratic volatility in explaining housing returns. While the related literature tends to focus on the dynamic role of volatility and housing returns within submarkets over time, our risk–return analysis is cross-sectional and covers the national U.S. metropolitan housing market. The study provides a number of important findings on the asset-pricing features of the U.S. housing market. Specifically, we find (i) a positive relation between housing returns and volatility, with returns rising by 2.48% annually for a 10% rise in volatility, (ii) a positive but diminishing price effect on returns and (iii) that stock market risk is priced directionally in the housing market. Our results on the return-volatility-price relation are robust to (i) metropolitan statistical area clustering effects and (ii) differences in socioeconomic characteristics among submarkets related to income, employment rate, managerial employment, owner-occupied housing, gross rent and population density.     

Measuring House Price Bubbles

Using data for six metropolitan housing markets in three countries, this article provides a comparison of methods used to measure house price bubbles. We use an asset pricing approach to identify bubble periods retrospectively and then compare those results with results produced by six other methods. We also apply the various methods recursively to assess their ability to identify bubbles as they form. In view of the complexity of the asset pricing approach, we conclude that a simple price–rent ratio measure is a reliable method both ex post and in real time. Our results have important policy implications because a reliable signal that a bubble is forming could be used to avoid further house price increases.     

To Sell or Not to Sell: Measuring the Heat of the Housing Market

Combining list‐price, sale‐price and time‐on‐the‐market data, we estimate an index that summarizes housing market conditions and that has a direct economic interpretation. The index measures seller's bargaining power in a structural search model of home seller behavior. Structural estimation uncovers an analytical relationship between reduced form coefficients of hedonic and marketing‐time equations and structural parameters. Thus, the index can be estimated using individual‐level or aggregate data. Using housing transactions data from the Washington, D.C., area, we show that index trends coincide with the up and downturns in home appreciation rates and with popular perceptions about the “heat” of the market.     

Pricing Relocation–Redevelopment Projects for City Expansion: The Case in China

A solution to city expansion under limited land availability is relocation of existing habitants, demolition of existing buildings and redevelopment of new buildings. In the case of Chinese cities, however, such strategies have become a channel for municipalities to increase their revenue from sale of land and productivity from increased development, at the expense of low compensation to former occupants and vacant housing after development. We utilize a sequential real options pricing approach to find conditions when relocation/demolition and redevelopment in a finite time horizon are optimal and also to show what factors the governments can influence to delay or speed up redevelopment.     

Effects of Macroeconomic Uncertainty and Labor Demand Shocks on the Housing Market

This article shows that macroeconomic uncertainty affects the housing market in two significant ways. First, uncertainty shocks adversely affect housing prices but not the quantities that are traded. Controlling for a broad set of variables in fixed‐effects regressions, we find that uncertainty shocks reduce both housing prices and median sales prices in the amount of 1.4% and 1.8%, respectively, but the effect is not statistically significant for the percentage changes of all homes sold. Second, when both uncertainty and local demand shocks are introduced, the effects of uncertainty on the housing market dominate that of local labor demand shocks on housing prices, median sale prices, the share of houses selling for a loss and transactions. The aforementioned effects are largest for the states that exhibit relatively high housing price volatilities, suggesting real options effects in the housing market during the times of high uncertainty.     

Foreclosure Discount: Definition and Dynamic Patterns

The lack of a consistent definition of foreclosure discount gives rise to discount rates that vary from nonexistent to sizeable across locations and time. We define the foreclosure discount as the discount of the real estate owned (REO) sale price relative to a normal‐sale estimated market value. With a dataset of 1.34 million REO sale transactions, across 16 CBSAs between 2000 and 2012, we find three noteworthy empirical findings. First, a high REO sale concentration in a market increases the foreclosure discount. Second, foreclosure discount is negatively related to recent house‐price appreciation. Third, the often reported high foreclosure discount for lower value properties is likely due to property condition.     

Modeling Spatial Variation in Housing Prices: A Variable Interaction Approach

The absolute location of each real estate parcel in an urban housing market has a unique location-value signature. Accessibility indices, distant gradients and locational dummies cannot fully account for the influence of absolute location on the market price of housing because there are an indeterminable number of externalities (local and nonlocal) influencing a given property at a given location. Furthermore, the degree to which externalities affect real estate values is not only unique at each location but highly variable over space. Hence, absolute location must be viewed as interactive with other determinants of housing value. We present an interactive variables approach and test its ability to explain price variations in an urban residential housing market. The statistical evidence suggests that the value of location, as embodied in the selling price of housing units, may not be separable from other determinants of value. It is recommended that housing valuation models, therefore, be specified to allow site, structural and other independent attributes to interact with absolute location—{ x , y } coordinates—when accounting for intraurban variation in the market price of residential housing. This approach is especially useful when estimating the value of housing for geographic areas where very little is known a priori about the neighborhoods or submarkets.     

House Price Changes and Idiosyncratic Risk: The Impact of Property Characteristics

Although the average change in house prices is related to changes in fundamentals or perhaps market-wide bubbles, not all houses in a market appreciate at the same rate. The primary focus of our study is to investigate the reasons for these variations in price changes among houses within a market. We draw on three theories for guidance, one related to the optimal search strategy for sellers of atypical dwellings, another focusing on the bargaining process between a seller and potential buyers and the third relying on the concept of land leverage. We hypothesize that houses will appreciate at different rates depending on the characteristics of the property and the change in the strength of the housing market. These hypotheses are supported using data from three New Zealand housing markets.     

House Search Duration in Hot and Cold Residential Markets

This study analyzes the determinants of house search duration of consumption‐driven buyers and individual investors in different housing market environments. We use data from surveys of recent house‐buyers in “hot” and “cold” housing markets in the 2000s housing bubble in California characterized by rising and declining residential house prices, respectively. The average house price and the surveyed geographical area are the same for both periods. Expected house ownership horizon is shown to be an important determinant of the realized search duration in addition to commonly considered housing and buyer characteristics. We find a statistically significant positive effect of it on the time until purchase in both housing price environments for consumption‐driven buyers. We also find that consumption‐driven house purchases were highly pronounced in coastal areas in the hot market and inland areas in the cold market. In contrast, long‐horizon investment activity leads that of consumption activity in those areas. Short‐horizon investors, on the other hand, concentrated their house search activity in inland areas in both housing market environments.     

Hedging House Price Risk in China

The increasing risk associated with China's housing prices is globally recognized. However, hedging this risk is challenging because of a lack of financial derivatives on China's housing assets. We suggest that the short sale of futures contracts for construction raw materials, i.e., iron ore or/and steel, can act as useful tools to hedge the systematic risk of China's new home price. We first present evidence that there is a strong and stable correlation between changes in China's housing prices and global steel/iron ore prices. Using a hedging strategy model, we then show that, during the sample period between 2009 and 2015, 20.6% of the total unpredicted variance in Chinese housing prices can be hedged by shorting rebar and iron ore futures. We further examine this strategy with an event study based on the announcement of the “home‐purchase restriction” policy in April, 2010. The cumulative abnormal returns show that both steel and iron ore prices reacted significantly to this negative shock, and therefore the proposed strategy could substantially help investors offset losses in the housing market. We finally provide some evidences that this strategy can also help investors in specific regional housing markets, or the resale housing markets.