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.     

The Long‐Run Relationship Between House Prices and Rents

I use standard error‐correction models and long‐horizon regression models to examine how well the rent–price ratio predicts future changes in real rents and prices. I find evidence that the rent–price ratio helps predict changes in real prices over 4‐year periods, but that the rent–price ratio has little predictive power for changes in real rents over the same period. I show that a long‐horizon regression approach can yield biased estimates of the degree of error correction if prices have a unit root but do not follow a random walk, and I construct bootstrap distributions to conduct appropriate inference in the presence of this bias. The results lend empirical support to the view that the rent–price ratio is an indicator of valuation in the housing market.     

The Impact of the Taxpayer Relief Act of 1997 on Housing Turnover in the U.S. Single‐Family Residential Market

The Taxpayer Relief Act of 1997 (TRA97) replaced a one‐time, post‐age‐55 capital gain exclusion with a larger gain exclusion amount that could be protected every two years without requiring that the taxpayer trades up in housing. This action had the potential to impact housing transactions for every existing homeowner, regardless of age, as well as future purchasers of housing. We analyze household‐level data to determine if the repeated ability to exclude periodic recognized capital gains on housing from taxation shortened housing tenure significantly after TRA97 became effective. We next consider whether the decline was heterogeneous across age groups, across trading up and trading down and across geography. Given that the impact of TRA97 appears at first glance to be most profound for taxpayers close to 55 years of age, a somewhat surprising result of our research is that significant decreases in tenure are pervasive, appearing in all age ranges and in samples of homeowners who trade up and who trade down. Finally, we provide additional evidence at the aggregate level that TRA97 led to measurable changes in the price elasticity of housing turnover in the four geographic regions defined by the U.S. Census Bureau (Northeast, Midwest, South and West) and in states that are home to large metropolitan housing markets.     

Do Conspicuous Consumers Pay Higher Housing Premiums? Spatial and Temporal Variation in the United States

This study is the first to examine the relationship between conspicuous demand and housing price dynamics. We hypothesize that conspicuous consumers would want high‐end homes to signal their wealth and this housing consumption behavior would induce greater deviations from fundamental house prices. We test this by using a unique dataset that matches the consumers’ appetite for nonhousing luxury goods from Google Insights for Search to housing premiums that they pay for high‐end houses in U.S. Metropolitan Statistical Areas (MSAs) during 2004–2011. The estimation results demonstrate that controlling for a wide range of MSA demographic and economic characteristics, conspicuous demand has a significant, positive relationship with housing premiums. This relationship varies spatially and temporally. Conspicuous demand has a stronger relationship with a price increase in high‐end homes in MSAs with a steady, higher housing premium than in MSAs with a volatile, lower premium during the boom period. In MSAs with a steady, higher housing premium, the relationship remains significant even during the bust period, potentially contributing to maintaining higher housing premiums.     

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.     

Idiosyncratic Risk of House Prices: Evidence from 26 Million Home Sales

This paper uses about 26 million home sales to measure house price idiosyncratic risk for 7,580 U.S. zip codes during three periods: (1) when the U.S. housing market was stable (1996–2000), (2) booming (2001–2007) and (3) busting (2007–2012), and investigates the determinants of house price risk. We find very strong relationships between risk and some basic housing market characteristics. There is a U‐shaped relationship between risk and zip‐code level median household income; risk is higher in zip codes with more appreciation volatility; and risk is not compensated with higher appreciation.     

Local House Price Diffusion

We contribute to the literature on house price diffusion by carrying out our analysis at three levels: CBSA (nationwide), town and census tract (Greater Boston Area). We estimate fixed‐effect models of house price growth on lagged growth (“Persistence”), nearby lagged growth (“Spillovers”) and Fundamentals growth. CBSA‐level Persistence and Spillover Effects are positive and significant. These large ripple/contagion effects likely contributed to the recent national‐level housing downturn. We find evidence of smaller town‐level Persistence and Spillover Effects. Hence, diffusion appears stronger across than within housing markets. Fundamentals and price expectations drive price diffusion, leaving room for bubbles from future price overoptimism.     

Competitive forces and strategic choice decisions: an experimental investigation in the United States and Japan

Managers operate in a complex, uncertain environment and tend to form simplified models in order to cope with this environment and make competitive strategic decisions (i.e., cost‐leadership, differentiation, or focus). In this study, we use an experimental design to examine the strategic choice decision‐making process in firms located in the United States and Japan. We develop several main‐effect propositions regarding managerial selection of competitive strategies, depending on the competitive forces (buyer power, threat of substitutes, threat of new firm entry, and high intensity of rivalry) they are facing. We propose a main effect due to country of origin: Japanese managers prefer a cost‐leadership strategy more than American managers do. We also propose several interaction effects regarding cross‐national differences in strategy selection between Japanese and U.S. managers. To test our propositions, we collected experimental data from 316 U.S. executives and 459 Japanese executives. We assessed relative impacts of the competitive forces on strategic decision‐making using a multilevel regression analysis. The research findings indicated that high buyer power and high substitution threat were associated with a preference for cost‐leadership strategies, and Japanese managers were significantly more likely to prefer a cost‐leadership strategy than U.S. managers. We also found that, under conditions of high buyer power, U.S. managers were less likely than Japanese managers to enter a market with a differentiation or focus strategy. We found little support for other interaction hypotheses, suggesting points of similarity between U.S. and Japanese managers. We conclude with a discussion of theoretical and managerial implications of our results. Copyright © 2002 John Wiley & Sons, Ltd.     

Evidence and Implications of Regime Shifts: Time‐Varying Effects of the United States and Japanese Economies on House Prices in Hawaii

We show that local house prices may be driven almost entirely by the demands of one identifiable group for several years and then by demands of another group at other times. We present evidence that house prices in Hawaii were subject to such regime shifts. Prices responded to demands associated with U.S. incomes and wealth for most years from 1975 through 2008. For about a decade starting in the middle of the 1980s, after the Japanese yen appreciated dramatically and Japanese housing and stock market wealth soared, however, house prices in Hawaii responded to Japanese incomes and wealth. Estimated models with these regime shifts outperformed conventional, constant‐coefficient models. The regime‐shifting model helps explain why, when and by how much the volatility and the elasticities of house prices in Hawaii with respect to the incomes and wealth of the United States and Japan varied over time.     

A Closer Look at the U.S. Housing Market: Modeling Relationships among Regions

This article investigates the dynamic interactions among nine U.S. regional housing markets by estimating the multivariate cointegration model using both autoregressive (AR) and moving average (MA) representations over the period from 1975 to 2010. Long‐run results indicate that the extent of convergence among the regional housing markets substantially increased over time and more so after the housing bubble burst in the latter part of 2006. Common stochastic trend analysis reveals that the housing regions of New England, Mid‐Atlantic and the Pacific were the primary regional drivers that led the regions toward long‐run equilibrium during the 1975 to 2006 subperiod. Further analysis indicates that the relationships among the regions cannot be attributed to trends in two important macroeconomic fundamentals: regional per capita income and regional GDP. Finally, short‐run analysis reveals substantial lead lag relationships among all the markets.     

Did Affordable Housing Legislation Contribute to the Subprime Securities Boom?

We use a regression discontinuity approach and present new institutional evidence to investigate whether affordable housing policies influenced the market for securitized subprime mortgages. We use merged loan‐level data on nonprime mortgages with individual‐ and neighborhood‐level data for California and Florida. We find no evidence that lenders increased subprime originations or altered loan pricing around the discrete eligibility cutoffs for the Government‐Sponsored Enterprises' (GSEs) affordable housing goals or the Community Reinvestment Act. Although we find evidence that the GSEs bought significant quantities of subprime securities, our results indicate that these purchases were not directly related to affordable housing mandates.     

Long‐Term Growth in Housing Prices and Stock Returns

A firm's long‐term stock returns are negatively related to past growth in housing prices in the state where the firm is located. The housing price effect is persistent and robust to controlling for the long‐term stock return reversal effect, changes in mortgage interest rates across the states, cyclicality in housing prices and overall local economic conditions. There is no evidence that extant asset pricing models can adequately explain the effect. The study discusses potential explanations for, and the implications of, the cross‐regional housing price effect.     

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.     

The Borrower's Perceived Risk in Mortgage Choice

We investigate how borrowers perceive the risk in the adjustable rate mortgage (ARM) versus fixed rate mortgage (FRM) choice. We develop a mortgage choice model where the coefficient on the long‐term bond risk premium is conditional on the borrower's perceived risk. We show that the perceived risk fluctuates over time according to the short‐term interest rate level and housing market conditions. We find that when the short‐term rate level is high (low), the borrowers perceive low (high) risk of a short‐term rate rise, thus opting for ARMs (FRMs). Also, during a down housing market they become more risk‐averse perceiving higher risk in choosing ARMs. The perceived risk level alters the borrowers’ sensitivity to the long‐term bond risk premium.     

The power and limits of modularity: A replication and reconciliation

Research Summary: We ask two questions: First, what are the underlying mechanisms that explain the power of modularity? Second, is the power of modularity robust in nonmodular problems? We replicate and then reconcile the key results in two prior models on modularity: E&L and S‐search. Our results yield several important insights. First, a significant portion of the advantage enjoyed by S‐search is attributed to multi‐bit mutation. Second, organization‐evaluation needs to be used in combination with multi‐bit mutation. Third, when the underlying problem structure becomes nonmodular, S‐search outperforms E&L search, even though the advantage is reduced. More generally, organizational designers need to pay close attention to how different elements of modular search interact, and avoid making incremental adjustments. Managerial Summary: Modularity in product or organizational design is an approach that divides a system into smaller modules and attempts to augment the system level performance by experimenting with new modules. Because of its potential benefits such as parallel problem solving, adaptability in turbulent environment, or high speed in experimentation, both scholars and practitioners subscribed to the “power of modularity” thesis. Despite its popularity, there are significant number of cases where the superiority of modular design does not hold. We compare and contrast two representative prior studies that had different views on modeling organizational evolution under a modular design principle. By doing so, we are able to uncover what contributes to the superiority of modular design. Our results suggest that, when conducting experimentation under a modular design, it is important to (a) experiment multiple decision components simultaneously within a single module; and (b) allow evaluation of the changes to be made by the module‐level manager not by the organization‐level manager. When the manager does not know whether the modularity in organizational design fits with the modularity in the task, it is advised to do multiple experimentation in a single module at a time while allowing the organization‐level manager to evaluate the changes.     

Explaining Asset Mispricing Using the Resale Option and Inflation Illusion

We investigate the overconfidence theory and inflation‐illusion hypothesis of asset mispricing. Both concepts address subjective asset valuation but place the impetus on differing explanations within the standard dividend‐growth model. We find that one of the theoretical outcomes of overconfidence—asset turnover—consistently explains mispricing in U.S. housing markets. Further, we find that asset turnover subsumes expected inflation in certain specifications, suggesting that dispersion in investors' beliefs is a better explanation of asset mispricing than the investors' inability to properly discount future cash flows.     

Alternative Lending Channels and the Crisis in U.S. Housing Markets

To what degree has the development of alternative mortgage funding channels promoted the recent boom and bust in U.S. housing markets? Past research examined whether Alt‐A and subprime market shares are correlated with the housing bubble. This article expands the analysis to include the share of specific “alternative” lending terms and finds that the shares of interest‐only and negative amortization loans are important factors in explaining the housing bubble. This result suggests that research on the housing market bubble should focus on the impacts of loan contract terms rather than loan channel.     

A New Measure of Real Estate Uncertainty Shocks

The objective of this article is twofold: first, we construct a new uncertainty measure that is specific to the real estate sector; second, we compare our uncertainty measure to other well‐established measures in the literature, such as the Macro Uncertainty (MU) by Jurado, Ludvigson and Ng. We show that our Real Estate Uncertainty (REU) measure accounts for twice as much of variation in housing prices—and starts compared to the MU. Furthermore, vector autoregressions and Granger‐causality analysis show that our uncertainty measure affects housing starts—and prices—in contrast to the other uncertainty measures that affect only housing starts.     

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.     

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.