The Journal of Real Estate Finance and Economics最新文献

The literature on the use of machine learning (ML) models for the estimation of real estate prices is increasing at a high rate. However, the black-box nature of the proposed models hinders their adoption by market players such as appraisers, assessors, mortgage lenders, fund managers, real estate agents or investors. Explaining the outputs of those ML models can thus boost their adoption by these domain-field experts. However, very few studies in the literature focus on exploiting the transparency of eXplainable Artificial Intelligence (XAI) approaches in this context. This paper fills this research gap and presents an experiment on the French real estate market using ML models coupled with Shapley values to explain the models. The used dataset contains 1,505,033 transactions (in 7 years) from nine major French cities. All the processing steps for preparing, building, and explaining the ML models are presented in a transparent way. At a global level, beyond the predictive capacity of the models, the results show the similarities and the differences between these nine real estate submarkets in terms of the most important predictors of property prices (e.g., living area, land area, location variables, number of dwellings in a condominium), trends over years, the differences between the markets of apartments and houses, and the impact of sales before completion. At the local level, the results show how one can easily interpret and evaluate the contribution of each feature value for any single prediction, thereby providing essential support for the understanding and adoption by domain-field experts. The results are discussed with respect to the existing literature in the real estate field, and many future research avenues are proposed.

This article investigates measurement errors when using indices to model house prices over time. Our analysis, comparing index prices to actual transaction values, reveals that in many cases, widely-used indices display measurement errors correlated with the index values. Measurement error correlated with predictors constitutes “differential measurement error” at the level of the data generating process (DGP). We further explore the presence of differential measurement error within the context of mortgage lending. Our findings uncover substantial measurement errors in mortgage data, which not only diminish the predictive accuracy of models but also introduce notable biases in the coefficient estimates of variables.

This paper is the first to empirically investigate the spillover effects of land value taxation. Using rich panel data for municipalities in Pennsylvania over the period 1980–2010, this study extends the existing research by offering the first evidence on the external impacts of land value taxation as well as the spatial dynamics of these impacts. The empirical model separately identifies the spillover effects of land value taxation and the externalities associated with traditional property taxation. The study shows that taxing land at a higher rate than structures on the land slows down employment growth in close neighbors but speeds up employment growth in neighbors within a longer distance. The findings suggest that land value taxation generates differential spillover effects across space. The paper discusses two underlying effects behind the observed differential impacts and opens up new avenues for further research.

This paper examines the response of equity and mortgage REITs to changes in the stance of monetary policy using an event study with intraday data. Monetary news around Federal Reserve’s announcements are decomposed into three types of surprise: changes in the current federal funds target rate, forward guidance and large-scale asset purchases. Estimation results show that monetary surprises have economically important and significantly negative effects on equity REITs. Around monetary announcements equity REIT returns are mostly spanned by US common stocks, while mortgage REITs maintain some distinct exposure to interest rate risks.

This study explores the effect of capital gains taxation on real estate transactions by comparing individual investors, that have greater timing ability and are more sophisticated, with owner-occupants. The study is based on a discontinuity in capital gains tax rates around the one-year holding period mark. The results document that investors that hold property for an investment purpose, relative to non-investors that are owner-occupants, exhibit a higher likelihood of selling immediately after the one-year holding period mark. Thus, frictions that inhibit optimal capitalization of tax rules differ based on investment and consumption objectives, i.e., for individual investors and non-investors. Additionally, the results depict the significance of sophistication and financial learning.

This study provides evidence of the causal impact of immigration on housing prices and rents using an extensive dataset from Germany that covers 382 administrative districts over the period 2004 − 2020. Employing a panel-data approach and a manually constructed Bartik instrument, we show that international migration has a significantly positive short-term effect on flat prices and rents. House prices are not significantly affected. We estimate that an increase in international migration of 1% of the initial district population causes a hike in flat prices of up to 3% as well as a hike in flat rents of about 1%. The increase in flat prices is more than twice as high as this at the lower end of the market, whereas the flat rental market demonstrates a more linear response. We also discover that immigration’s impact on flat prices and rents does not significantly differ across rural and urban areas within the country.

After the great turmoil of the latest financial crisis, the criticism of the regulatory frameworks became increasingly stronger. The rules that banks needed to comply with are presumed to be procyclical and unable to prevent and mitigate the extent of strong financial and economic cycles. As a result, Basel III introduced a set of macroprudential tools to overcome these regulatory shortfalls. One tool that strives to counteract the issue of procyclicality is the countercyclical capital buffer ((CCyB)). This paper introduces a heterogeneous agent-based model that investigates the implication of the new regulatory measure. We develop a housing and a financial market where economic agents trade residential property that is financed by financial institutions. To examine the macroeconomic performance of the (CCyB), we evaluate the dynamics of key stability indicators of the housing and the financial market under four different market conditions: in an undisturbed market and in times of three different structural shocks. Computational experiments reveal that the (CCyB) is effective in stabilizing the housing and the financial market in all market settings. The new macroprudential tool helps to mitigate economic fluctuations and to stabilize market conditions, especially in the aftermath of a crisis. It is not able to prevent any of the crises tested. However, the extent of the stabilizing effect varies according to market conditions. In the shock scenarios, the (CCyB) performs better in dampening market fluctuations and increasing banking soundness than in the base scenario.

This article conducts a comprehensive analysis of the potential returns from investing in residential real property, with a special interest in any added gains due to trading larger volumes. Since the literature suggests that they are at a bargaining disadvantage, one notable finding is that individuals earn price benefits like so-called professional investors. Upon controlling for myriad well-known bargaining channels as well as investors’ demand characteristics, we find the mean price gains due to bargaining for individuals range from 4.2% for the lower volume investors (3–4 homes) to more than 7% for the highest volume traders (13 + properties). These average returns are for both buyers and sellers. Professionals exhibit bargaining acumen beginning with their first transactions (6.4%) and garner returns like individuals at higher volume levels (7.3%). The finding of investors’ bargaining effects generally increasing with greater volume implies gains in asymmetric information possibly through learning and/or reduced search costs.