The Journal of Real Estate Finance and Economics最新文献

The conventional belief regarding the frequency of property assessment has been that annual revaluation is optimal. Practices, however, vastly differ from this norm and the length of assessment cycles diverge widely across tax assessing jurisdictions. This study fills a gap in the literature by examining the relation between cycle length and assessment outcomes, using panel data from the early 2000s to 2016 in Virginia and New York as two representative states. We examine the conditional correlation between revaluation lag and assessment uniformity among tax assessing jurisdictions in Virginia. We find that assessment uniformity tends to be lower by three to six percent when revaluation is delayed by an additional year; however, the rate of deterioration does not vary across jurisdictions with different cycle lengths. In New York, we find that switching from irregular to annual assessment is positively associated with assessment uniformity and administrative costs.

Abstract

This paper examines the effect of peer sentiment on firm production decisions using data from public homebuilders in the US. Peer sentiment is measured by the NAHB/Wells Fargo Housing Market Index, derived from a monthly survey of homebuilders’ perceptions about the housing market. A one-standard-deviation increase in the peer sentiment index induces an average builder to increase their land inventory by 6.4% (4,937 lots) and building expenses by 5.9% ($34.5 million). The effect is weaker when firms are highly divided in their opinions. Following peer sentiment does not affect stock price performance, but overbuilding is associated with a lower return-on-asset.

Conditioned geographical clustering is the strategy of grouping portions of a REIT’s property portfolio within a contiguous region to exploit economies of scale through spatial proximity. This paper examines the impact of conditioned geographical clustering on REIT operational efficiency and value. Our results suggest REITs create value by employing a strategy of property clustering and that operational efficiency is the primary channel through which increases in value are achieved. In addition, results suggest conditioned geographic clustering mitigates the REIT geographical diversification discount. Our findings support an optimal degree of property clustering within the 5th to 35th percentiles of the sample distribution and suggest the optimal cluster size has a radius between 50 and 75 miles.

This study develops a theoretical model examining the relation between housing market strength and brokerage choice. Our model shows that although internal transactions (where both buyer and seller agents are either the same or work for the same firm) have the potential side benefits of higher commission rates and lower search costs, in a strong housing market, brokerage firms are more likely to engage external transactions because of the greater demand for housing. However, when the market weakens, external demand for housing decreases, and brokerage firms become more willing to conduct internal transactions. Furthermore, while an internal transaction tends to occur at the expense of lowering the selling price, we show that it could also be chosen by brokerage firms with higher in-house searching-matching efficiency. This higher in-house efficiency generates a (second-order) counterforce of increasing the price. Hence, our model demonstrates that the housing market has a (partial) self-correction mechanism for the principal-agent incentive misalignment problem, especially when the market strengthens. Conversely, when the market weakens, internal transactions increase and prices decline, which can further weaken the market. Therefore, the equilibrium brokerage choice creates a self-reinforcing mechanism for generating more extreme market conditions. Using the Doubly Robust (DR) estimation method, we present empirical evidence consistent with the model with multiple listing service data from Hampton Roads, Virginia.

Recent housing bubbles in OECD countries have been accompanied by large-scale household debt buildups and rising homeownership rates, and have generally occurred in jurisdictions with soft legal limits to loan-to-value (LTV) ratios. We show that all these empirical features can be rationalized within a simple political economy framework of macroprudential regulation, where household debt is secured by housing collateral and is constrained by LTV caps. Specifically, we study an overlapping generations model in which non-altruistic households exhibit heterogeneous tastes for housing tenure. Optimal tenure arrangements may require collateralized debt, which risk-neutral banks supply given the prevailing regulatory framework. Under majority rule, housing bubbles can generate their own electoral support: when collateral values are rationally expected to climb, relatively lax financial regulation is favored by both middle-class mortgage applicants and high-income homeowners, who fear house price reversion to market fundamentals. Home buyers’ beliefs about house price inflation then fuel increasing household leverage across income classes, resulting in a self-confirming housing bubble with widespread homeownership.

A growing share of mortgages display some financial assistance from sellers, or seller contributions, leading to an inflated transaction price thus distorting the loan-to-value ratio (LTV). Our study finds that such loans have sharply increased rates of delinquency, even after accounting for this LTV distortion effect. Further, when contributions are more likely to have been requested by the buyers, instead of the sellers bringing them to the bargaining table, the association with delinquency is clearest. These contributions signal buyers’ potential liquidity constraints which may make them more vulnerable to financial shocks after origination, thus more likely to enter delinquency.

This paper evaluates the ability of various indicators related to macroeconomic fundamentals, credit conditions, and housing supply to predict house price growth in the United States during the post-financial crisis period. We find that the inclusion of different measures of housing supply indicators significantly improves the forecasting performance for the period of 2010-2022. Specifically, incorporating the monthly supply of new homes into a VAR model with house price growth reduces the RMSE by over 30 percent compared to a univariate benchmark. Moreover, forecasting accuracy improves further at a longer forecast horizon (greater than three months) when the mortgage rate spread is also used as a predictor. Further improvements are made if "Direct" forecasts are used instead of iterative forecasts. The shrinkage method like LASSO shows that the monthly supply of new homes is an important predictor at all forecasting horizons, while the mortgage spread is most relevant for longer forecast horizons.