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This paper uses an assignment model to understand the cross section of house prices within a metro area. Movers' demand for housing is derived from a life-cycle problem with credit market frictions. Equilibrium house prices adjust to assign houses that differ by quality to movers who differ by age, income, and wealth. To quantify the model, we measure distributions of house prices, house qualities, and mover characteristics from micro-data on San Diego County during the 2000s boom. The main result is that cheaper credit for poor households was a major driver of prices, especially at the low end of the market.

While filtering has long been considered the primary mechanism by which markets supply low-income housing, direct estimates of that process have been absent. This has contributed to doubts about the viability of markets and to misplaced policy. I fill this gap by estimating a \"repeat income\" model using 1985-2011 panel data. Real annual filtering rates are faster for rental housing (2.5 percent) than owner-occupied (0.5 percent), vary inversely with the income elasticity of demand and house price inflation, and are sensitive to tenure transitions as homes age. For most locations, filtering is robust which lends support for housing voucher programs.

The employment-to-population ratio among prime-aged adults aged 25–54 has fallen substantially since 2000. The explanations proposed for the decline in the employment-to-population ratio have been of two broad types. One set of explanations emphasizes cyclical factors associated with the recession; the second set of explanations focuses on the role of longer-run structural factors. In this paper, we argue that while the decline in manufacturing and the consequent reduction in demand for less-educated workers put downward pressure on their employment rates in the pre-recession 2000–2006 period, the increased demand for less-educated workers because of the housing boom was simultaneously pushing their employment rates upwards. For a few years, the housing boom served to “mask” the labor market effects of manufacturing decline for less-educated workers. When the housing market collapsed in 2007, there was a large, immediate decline in employment among these workers, who faced not only the sudden disappearance of jobs related to the housing boom, but also the fact that manufacturing's steady decline during the early 2000s left them with many fewer opportunities in that sector than had existed at the start of the decade.

This paper develops a dynamic model of neighborhood choice along with a computationally light multi-step estimator. The proposed empirical framework captures observed and unobserved preference heterogeneity across households and locations in a flexible way. We estimate the model using a newly assembled data set that matches demographic information from mortgage applications to the universe of housing transactions in the San Francisco Bay Area from 1994 to 2004. The results provide the first estimates of the marginal willingness to pay for several non-marketed amenities—neighborhood air pollution, violent crime, and racial composition—in a dynamic framework. Comparing these estimates with those from a static version of the model highlights several important biases that arise when dynamic considerations are ignored.

Purpose The study aims to analyze the effects of the Covid-19 pandemic on house prices. Design/methodology/approach The authors start by discussing the possibility that house price indexes may not fully incorporate the effects of the pandemic as of yet. Against the background of the pandemic, the authors then analyze economic and behavioral effects affecting house prices. The authors also discuss how the linkages between tourism and house prices have been affected. The authors further present evidence of an emerging shift in preferences from urban locations to more peripheral ones. Findings The authors report variance in the evolution of house prices across countries at the onset of the pandemic, with locations depending heavily on tourism showing slower price appreciation while appreciation has firmed in other places. The authors argue that the resilience of house prices is not only because of the low-interest rate environment and government efforts to support firms and households, but also behavioral factors. In some locations, the price of condominiums has declined relative to the price of detached houses. This could indicate that wealthier households are seeking more space and larger units as a result of the crisis. There is also evidence of a downward pressure on rents, leading to increased price–rent ratios in the USA. Originality/value By considering both economic and behavioral factors, this paper provides for a better understanding of the resilience and realignment of house prices at the onset of the Covid-19 pandemic.

Using data from the PSID, we estimate a dynamic model of housing demand with nonconvex adjustment costs, credit constraints, and uncertainty about income and home prices. We simulate how consumer behavior responds to house price and income declines as well as tightening credit. In response to a negative home price shock, households early in the life cycle climb the housing ladder more quickly and invest more in housing assets due to the lower price. With a concurrent negative income shock, however, housing demand falls among young and middle aged households who stay in smaller homes rather than to trade up.

This study explores integration and risk transmission in the Chinese (residential) housing market using variance decompositions from the vector autoregression model. The study covers the period from January 2000 to September 2022. The results indicate that short-term total connectedness changes over time and is significantly larger than long-term connectedness, suggesting that total connectedness is sensitive to time-specific developments and short-term events. Supply-side shocks typically lead demand-side shocks in the long term. However, because of changes in the economy, market conditions, government policies, and investor sentiment, the direction of short-term risk transmission between supply and demand sides varies. Moreover, long-term shocks usually flow from housing prices to the supply side and from the demand side to housing prices. In the short term, prices alternately affect the supply and demand sides of the housing market at different points in time. This study also supports the predictive ability of connectedness measures for macroeconomic conditions, suggesting meaningful implications for investors, real estate developers, and policymakers.

The conventional wisdom that homeownership is very risky ignores the fact that the alternative, renting, is also risky. Owning a house provides a hedge against fluctuations in housing costs, but in turn introduces asset price risk. In a simple model of tenure choice with endogenous house prices, we show that the net risk of owning declines with a household's expected horizon in its house and with the correlation in housing costs in future locations. Empirically, we find that both house prices, relative to rents, and the probability of homeownership increase with net rent risk.

Housing policy, as well as academic research, are increasingly concerned with the role of bias in subjective dwelling valuations as a proximate measure of households’ house price expectations and their relationship with housing demand. This paper contributes to this area of study by exploring the possibility of simultaneous relationships between households’ price expectations and incentive to maximise the size of housing services demanded also accounting for the supply side factors and regional perspective. The empirical estimation takes the form of a system of a two simultaneous equations model applying two stage least squares estimation technique. Cross sectional estimations utilise data extracted from the Israeli Longitudinal Panel Survey (LPS) data. Applying the best available proxy for households’ price expectations, calculated as the ratio between subjective dwelling valuations (LPS) and the estimated market value of the same properties, research has identified the interrelated factors that simultaneously influence householders’ price expectations and housing demand. Results offer conceptual and empirical advantages, highlighting the imperfect nature of the housing market, as reflected by the inseparability of bias in subjective valuations and housing decisions.

Through the collateral channel for entrepreneurs, a positive housing demand shock in Liu et al. [(2013) Econometrica 81, 1147–1184.] increases land prices and business investment, but consumption decreases on impact and there is thus a comovement problem. This paper improves Liu et al. [(2013) Econometrica 81, 1147–1184.] by adding general household preferences with broader intratemporal and intertemporal substitutions Bayesian estimation of our structural model based on aggregate US data suggests that the intratemporal substitution is larger than unity and the intertemporal substitution is smaller than unity. Our impulse responses show that a positive housing demand shock increases land prices, business investment, and consumption, which resolves the comovement problem. Moreover, the strength of the collateral channel linking land prices and business investment in our Bayesian DSGE model is larger than that in Liu et al. [(2013) Econometrica 81, 1147–1184.]. Housing demand shocks explain 39−43% of the variance of output and 41−47% of the variance of investment in our model, but the same shocks explain only 17−31% of the variance of output and 30−41% of the variance of investment in Liu et al. [(2013) Econometrica 81, 1147–1184.]. Variance decomposition reveals that housing demand shocks account for a larger share of the fluctuations in land prices, investment, employment, and output than other shocks. Using the marginal data density as the measure of fit for models, we find that our model can better explain the same US aggregate data.