Bay Area Walk score premiums : unlocking value through neighborhood trends

Abstract

The digital age of real estate provides access to new data and techniques to evaluate properties. Real estate brokerage and technology firms are assembling this data to produce user-friendly scores that serve as powerful metrics to identify real estate trends and evaluate buyer behavior. This paper examines Redfin's "Walk score" that measures a location's walkability to amenities like grocery stores or parks and uses a hedonic pricing model to find the $/square-foot premium for high Walk scores in three communities in the San Francisco Bay Area. The data is composed of residential transactions from 2014 to early 2016 that are analyzed at the neighborhood level and normalized to improve the precision of the hedonic model. This neighborhood lens produces a more robust analysis than the broader data sets used in the majority of prior Walk score research. The results shown in this paper demonstrate that a high Walk score is highly correlated with increased property values in a broad range of communities with diverse socioeconomic characteristics. This study includes a framework for using Walk scores (and several related scores) by discussing the composition of the scores, economic principles underpinning them and the critical assumptions for hedonic regressions using Walk scores. These considerations are critical to assessing the real premium of Walk scores. The paper concludes with an analysis method for investors to use walk scores to identify real estate home-buying trends, find under-valued property and create development programs that leverage and build upon walkability.