This study explores the possible impacts of short-term house trades, often called “house flips,” on local housing markets. Using nationally representative data across multiple U.S. housing markets during 2000–2013, we test the Spillover Hypothesis and the alternative Competition Hypothesis for the influences of flips on the neighborhood non-flip house transactions. We find that the local flip tendency, our measurement for local flip activity, is negatively associated with the holding durations of neighborhood houses experiencing non-flip resales, and positively associated with their resale prices, after we control for market conditions that affect both flip and non-flip transactions as well as other potential determinants for holding duration and price. The effects are generally more prominent when the local flip tendency is measured for a longer run, supporting the Spillover Hypothesis. These effects are also persistent regardless of whether the local flip tendency is defined as the proportion of flips in the local resales by transaction number or by transaction volume, or whether it is measured continuously or discretely. The results also hold when we endogenize the local flip tendency, or when we change the neighborhood size. These findings demonstrate the substantial influences of flip activities to the local housing market turnover and price movements.

The primary purpose of this study is to explore whether a shock to homebuilder sentiment generates similar effects on single-family housing activities across regions in the United States. Our findings show that of the four census regions, the South is the most sensitive to a sentiment shock, whereas the Northeast is the least sensitive. Regional differences in economic as well as noneconomic factors appear to drive the differential impacts.

This paper examines the influence that community bank residential mortgage lending has on regional housing prices and ultimately the housing price bubble that culminated with the Great Recession of 2007–09. Previous research suggests that community bank lending is influenced by financial intermediation frictions, such as difficulty attracting uninsured deposits and limited access to the bond market. Such frictions cause bank liquidity to play an important role in the ability of community banks to navigate swings in the local economy as well as real estate prices. We take these frictions as the starting point in our analysis and contribute to literature by focusing on the impact that community banks exert on housing prices. Specifically, we find that while community bank residential mortgage holdings are marginally related to housing price cycles, their mortgage lending does not contribute in an economically meaningful way to housing price bubbles. Even though bank liquidity, defined as the ratio of securities-to-assets, plays a role in the degree of community bank residential mortgage lending, it is not sufficiently strong enough to influence housing prices.

We provide first evidence of the economic factors that motivate individuals to obtain a real estate license to potentially pursue a career in real estate sales. We focus on four key housing indicators: median house price, median days on the market, housing affordability, and housing sales performance. Our results indicate that house price is the main driving force for individuals to take the California real estate salesperson license exam as well as for those real estate salespersons wanting to upgrade to the broker license.

Tax increment financing, a form of overlay zoning, are purported to spur economic development and promote the public interest of the community. However, these municipal overlay zones frequently generate hostility due to their use of public funds. While a large volume of knowledge exists on the impact of residential real estate prices within a tax increment financing district, little is known about the impact of the municipal overlay zone on the surrounding community. Using four, similarly timed, but separate tax increment financing districts and a transactional residential real estate dataset, we test price implications for residential real estate property at three separate geographic distances. Using propensity score matching and fixed effects modeling, we find that the imposition of tax increment financing districts increased surrounding residential real estate property values.

This paper is devoted to the relationship between news sentiment and changes in housing market movements. It provides a novel and straightforward approach to account for heterogeneous expectations of market actors within a probabilistic framework utilizing machine learning. Our novel sentiment index shows a persistent and statistically significant explanatory power for the prediction of the housing market, in contrast to common dictionary approaches. This holds for news headlines and abstracts and different definitions of sentiment indices. Our results can be regarded as the first sentiment-based evidence of heterogeneous actors in the housing market and underline the importance of different expectations for measuring non-fundamental drivers.

This study examined spending behaviors of U.S. tenants who reported delaying rent payments during federal eviction moratoria in 2020-2021, enacted in response to the Coronavirus Disease-2019 (COVID-19) pandemic. A national sample of 772 middle and low-income tenants who reported delaying rent payments because of the eviction moratoria were assessed from May 2020 to October 2020. Among tenants who delayed paying rent, most rent money was spent on groceries (11-19%), utilities (9-14%), substance use (8-10%), and debt (7%) across two time periods; the remaining rent money was spent on other expenses including recreation and medical care. Sociodemographic and psychiatric characteristics together only explained 2-3% of the variance in spending in major expense categories suggesting the broad impact of the COVID-19 pandemic. Together, these findings provide insight into spending behaviors of tenants during a time of great financial and psychological distress.

A sound understanding of real estate markets is of economic importance and not simple, as properties are a heterogenous asset and no two are alike. Traditionally, parametric or semi-parametric and, thus, assumption-based hedonic pricing models are used to analyze real estate market fundamentals. These models are characterized by the fact that they require a-priori assumptions regarding their functional form. Usually, the true functional form is unknown and characterized by non-linearities and joint effects, which are hard to fully capture. Therefore, their results should be interpreted with caution. Applying the state-of-the art non-parametric machine learning XGBoost algorithm, in combination with the model-agnostic Accumulated Local Effects Plots, (ALE) enables us to overcome this problem. Using a dataset of 81,166 residential properties for the seven largest German cities, we show how ALE plots enable us to analyze the value-determining effects of several structural, locational and socio-economic hedonic features. Our findings lead to a deeper representation of real estate market fundamentals.

We investigate the risk of claims using hazard models for house insurance for the three periods: prior to, during and after the financial crisis. The present study is based on the data provided by Property & Casualty Carrier for the Canadian Market. The semiparametric Cox proportional hazard model results suggest that Canada’s house insurance options (Bronze, Silver, Gold, and Platinum) influence the risk of claims. The smaller coverage option, Bronze, has much fewer claims than the complete coverage option, Gold. This may suggest the presence of a moral hazard problem. The financial crisis however impacted the role of the type of coverage had on the risks of claims reducing the moral hazard problem, after controlling for the characteristics of the houses.

In real estate pictures, home staging, which is the temporary display of high-end furniture, is meant to positively impact the sales process for the involved parties: the broker, the seller and also the buyer. In this article we focus on the impact of home staging on the price aspect in this process. In our experimental design to isolate the effect of furniture on valuation, we show pictures with furniture to one group and those without furniture to the other group. The main task for both groups of students is to estimate the market value of an empty apartment. Additionally, they must state their preferences for furniture in an apartment. On aggregate, we do not find significant differences in market value estimates between the two conditions, which is in line with the recent literature. At subsets based on individually indicated preferences for furniture, we find two main differences: those in the group with lower indicated preferences for furniture do not adjust their value estimates in a second estimation task, where the presence of furniture is changed, while those in the second group adjust their value estimates in the second estimate, where the adjustments are significant with a magnitude of approximately 11% based on their preferred condition. We conclude that if a client indicates a high preference for furniture, then the presented furniture affects his or her perception of the property. Additionally, we monitor the dwell time, which is significantly longer for the furnished condition and is an indication of information reception.