Real estate property listings use specific language to market properties to a target buyer – typically one that will garner the largest profit. As home-seekers have different preferences for house characteristics and neighborhood amenities, the words used to advertise homes are expected to vary according to the type of neighborhood and expected homebuyer. In this article, we develop a framework for extracting the key characteristics used to advertise properties according to the racial and income profile of home mortgage applicants in different types of neighborhoods. We perform an exploratory text analysis on words according to neighborhood types and use a binomial logistic regression model to determine the most discriminatory words for each type of neighborhood. Finally, we assess the ability of the property listing text to predict the type of neighborhood the property belongs to. Using a small, illustrative case study of listings from Charlotte, North Carolina, we find that the presence of specific neighborhood names holds more importance in neighborhoods with primarily White homebuyers. In gentrifying neighborhoods, unique property characteristics such as parquet flooring, and words associated with revitalization near the city center are common. Listings in neighborhoods with minority homebuyers are less likely to mention schools and feature traditionally suburban descriptors such as cars, garage, and roadways. We envision that this framework, using near real-time data sources, holds the potential to advance neighborhood prediction efforts, our understanding of amenity preferences and sorting patterns, and to illuminate less visible processes of change such as discrimination in the housing market.

房地产物业列表使用特定的语言向目标买家推销物业-通常是一种能够获得最大利润的语言。由于寻求住房者对房屋特性和邻里便利设施的偏好不同，因此用于广告房屋的字词可能会根据邻里类型和预期的购房者而有所不同。在本文中，我们开发了一个框架，用于根据不同类型社区中房屋抵押贷款申请人的种族和收入状况，提取用于宣传房地产的关键特征。我们根据邻域类型对单词进行探索性文本分析，并使用二项式逻辑回归模型确定每种邻域类型中最具歧视性的单词。最后，我们评估属性列表文本预测属性所属社区类型的能力。通过对北卡罗来纳州夏洛特市的房屋列表进行的小型说明性案例研究，我们发现，在主要有白人购房者的社区中，特定社区名称的存在更为重要。在高档化的社区中，镶木地板等独特的房地产特征以及与市中心附近的复兴相关的词语很常见。在少数族裔购房者附近的房源中，很少提及学校，并且通常采用郊区的描述语，例如汽车，车库和道路。我们设想，该框架使用近乎实时的数据源，具有推动邻里预测工作，我们对便利设施偏好和排序方式的理解，