Alan Tidwell

Previous research demonstrates that housing prices frequently move in tandem across regions, underscoring the interconnectedness and correlation present within housing markets. Building on this foundation, our study advances the analysis by examining quantile co‐movements and the synchronization between local and national housing markets. Using the quantile factor model across the full distribution of housing prices, we identify distinct factor structures at the lower and upper tails that contrast with those observed in the middle of the distribution. This analytic framework enables the detection of previously hidden factors influencing housing markets. With this approach, we illuminate how housing price dynamics interact across market segments, price levels, and geographic areas. Our findings reveal that co‐movements can vary substantially across low, stable, and high housing price regimes, thus providing more comprehensive and nuanced economic insights into the complex nature of housing price fluctuations.

Using semantic analysis of newspaper content, we introduce a novel index that reflects the public perception of real estate uncertainty ( NewsREU ). The NewsREU is model‐free and offers straightforward interpretability. We benchmark the NewsREU against a model‐based real estate uncertainty ( REU ) measure and show that the NewsREU contains incremental information about the real estate sector beyond what the REU captures. Moreover, we utilize an advanced topic model, leveraging neural inference network and contextual embeddings, to effectively unveil the underlying themes in narratives surrounding real estate uncertainty. This approach allows for decomposition of the NewsREU into distinct Topic NewsREU components that may reflect different facets of uncertainty within the realm of real estate. We find that different types of real estate uncertainty may either positively or negatively forecast future housing outcomes. These results underscore the distinct contribution of our real estate uncertainty indices in advancing our understanding of the role of real estate uncertainty.

The use of social networking sites (SNS) within professional contexts is increasingly prevalent, yet studies examining their impact within the real estate sector remain limited. In this study, using LinkedIn followers as an indicator of professional networks, we investigate the effect of professional networking on performance within the commercial real estate market. By leveraging an agent's LinkedIn followers, we introduce a novel measure of professional network into the real estate literature. Findings suggest that listing agents with a larger number of SNS followers tend to engage in more property transactions, achieve higher sales prices, and experience shorter sale times than agents with fewer SNS connections. Similarly, agents considered “active” on the SNS also perform better than agents that have an account but are not “active.” These findings persist after accounting for various agent, property, and location characteristics, suggesting that SNS can be a helpful tool for real estate agents in utilizing and expanding their professional network.

The electorate is concerned with personal financial and macroeconomic conditions in addition to policy issues and tends to hold the incumbent party accountable when voting. The performance of the United States’ largest asset class, residential real estate, should influence individual voter behavior. According to economic voting theory and the “homevoter” hypothesis, homeowners will be more supportive of policies that are perceived as beneficial to their property value. We investigate this relationship in the US residential real estate market by evaluating the effects of heterogenous county-level housing market performance on voter behavior in national presidential elections and find counties with superior house price performance in the four years preceding an election are more likely to “vote-switch” to the incumbent party. Counties with relatively inferior house price performance in the four years leading up to the election are more likely to switch their vote from the incumbent to the challenging party. The relationship is strongest in the years closest to an election and in counties that rank in the higher quartile of housing price performance. Election outcomes in “swing” counties are particularly vulnerable the local real estate economy. To our knowledge, this is the first study to link heterogeneous local residential real estate performance over a series of national election outcomes.

This study analyzes the time‐varying nature and determinants of comovements in US housing prices using state and metropolitan statistical area (MSA) data. We employ dynamic factor models with time‐varying loadings and stochastic volatility (DFM‐TV‐SV) to estimate the national, regional, and state factors. The time‐varying factor loadings and stochastic volatility features enrich the dynamic factor model structures and are an effective tool to examine the comovements in housing prices. We find that the national factor is the dominant factor in explaining the movement of housing prices. The national factor accounts for 79% of the variation in state‐level housing prices on average, with the greatest magnitude occurring during the housing boom and bust periods in many regions and states. We also find that the factors and synchronization effects are time‐varying and heterogeneous across regions. The state‐level housing prices contain higher national housing factor components in states with more diverse economies, higher wages and house prices, and lower unemployment rates. These findings shed light on the effectiveness of residential real estate diversification across the United States and the potential for elevated national housing risk amid economic downturns due to increased national housing price integration.

Gender differences in labor market outcomes are well-documented and are expected to be more pronounced in male-typed domains such as commercial real estate. In this study, we expand the literature examining gender dynamics in the labor market, focusing specifically on gendered parity in commercial real estate brokerage. Employing robust procedures, we find that the unadjusted observed male agent property sales price premium and shorter marketing time are completely absorbed by property and market attributes as male agents list/sell properties with superior property characteristics, specifically larger and higher quality properties, when compared to female agents. The findings suggest that initial gendered differences in sales price and TOM are due to industry-driven agent selection, in which women do not fully participate in the largest and best part of the office market. Despite these findings showing gender performance neutrality, female agents are involved in significantly fewer property transactions than their male counterparts. We find evidence that this unequal labor outcome is associated with a type of affinity bias known as 'homophilic’ behavior, in which buyers and sellers prefer to work with agents of their same gender, resulting in more male agent representation.