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How do real-estate investors make decisions about where to purchase properties? In recent years, an increasing share of the market has gone to investors, making it critical that we understand the behaviors of these important actors. This study examines investment as a relational process. Through 74 interviews with real estate investors, as well as additional interviews with homeowners and renters in low- to middle-income neighborhoods in Baltimore, I find that strategic interactions and intimate relational work among professional and amateur investors are key to understanding how properties are sold. Professional investors cultivate bonds with new investors by identifying common goals and obstacles. They also use concealment strategies to sell homes, including: legal concealment, concealment through bundling, and concealment through brokers. Legal concealment involves using legal vehicles to transfer properties between owners. Concealment through bundling occurs when properties are bundled together to be sold in bulk. Finally, concealment through brokers entails finding brokers who can connect sellers to investors from other markets. These strategies obscure not only the nature of the relationship between buyers and sellers but also the quality of products being sold. The series of exchanges resemble how products are sold within direct-selling organizations and other predatory industries, which rely on a pyramid-like structure of sponsors and distributors. Furthermore, concealment strategies are more likely to happen in weaker markets and poorer neighborhoods where investor behavior is less constrained—this suggests that concealment has potential to increase urban inequality. [ABSTRACT FROM AUTHOR]

Large-scale text data from public sources, including social media or online platforms, can expand urban planners’ ability to monitor and analyze urban conditions in near real-time. To overcome scalability challenges of manual techniques for qualitative data analysis, researchers and practitioners have turned to computer-automated methods, such as natural language processing (NLP) and deep learning. However, the benefits, challenges, and trade-offs of these methods remain poorly understood. How much meaning can different NLP techniques capture and how do their results compare to traditional manual techniques? Drawing on 90,000 online rental listings in Los Angeles County, this study proposes and compares manual, semi-automated, and fully automated methods for identifying context-informed topics in unstructured, user-generated text data. We find that fully automated methods perform best with more-structured text, but struggle to separate topics in free-flow text and when handling nuanced language. Introducing a manual technique first on a small data set to train a semi-automated method, however, improves accuracy even as the structure of the text degrades. We argue that while fully automated NLP methods are attractive replacements for scaling manual techniques, leveraging the contextual understanding of human expertise alongside efficient computer-based methods like BERT models generates better accuracy without sacrificing scalability.

The main topic of the paper was to identify the types of development in the field of NUTS 3 level housing markets. At first, we have to calculate the 'real' size of housing markets in that level. We used the database of Hungarian Central Statistical Office (HCSO), where number of transactions (used flats and houses) and average selling prices were available. Using the basic data, the results was deflated to see the long-term changes (15 years) in comparable prices. At the next step the correlation was measured between the change of housing market size at NUTS 3 level and 7 chosen indicators, closely related to developmental studies (e.g. purchasing power parity per capita, level of active workers, unemployment). As the result, we got 5 group of counties, containing 4-4 NUTS 3 units. Three of them showed a very strict similarity between the units, what we called industrial, 'Great Plain' and southern peripheral types. In the case of the other two groups (north-western and central types) the picture is a bit mixed. There are three of the indicators where the correlation levels differ from each other. One was population change, the second was the PPP-percapita level where the discrepancies came from the relative openness of NUTS 3 level housing markets, influenced by buyers out of the unit (from other part of Hungary, or from abroad - e.g. Lake Balaton district, western border zone, Bratislava agglomeration zone affecting some settlement on the Hungarian side of the border). The third one was GDP per capita, which suggest, the increasing level of GDP does not reflect the similarly increasing amount of earnings using for buying a flat. [ABSTRACT FROM AUTHOR]

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The UK's city centre apartment markets have been affected by the coronavirus pandemic and a building safety crisis in ways not experienced by its suburban and rural housing markets. Sellers and estate agents have encountered falling demand and prices, elevated safety concerns, reluctant lenders and changes in buyers' preferences. Against this backdrop, we investigated the narratives and images used to sell what have sometimes appeared to be 'less sellable' homes. Analysing the textual and visual content of 100 adverts for city centre flats, we explored the possible effects of the pandemic on property advertising, the positioning within adverts of building safety and, noting growing interest in sustainability, the presence of sustainability messages. Findings suggest that the core narratives used to sell city centre flats remain largely unchanged from those deployed to first market the concept of 'city living' to UK buyers in the late 1990s. Messages about building safety and sustainability appear uncommon. The implications of the findings are considered. [ABSTRACT FROM AUTHOR]

The housing market in the UK features a mortgaging system where interest rates are either fixed for short periods (typically 2 or 5 years) or varied to track interest rates of the Bank of England base rate. The reactions of home buyers and investors to changes in the mortgage rate have impacts on the buy-to-let housing market, and this in turn impacts tenants who are renting from private landlords. Such reactions become more significant when there are financial shocks, as occurred in 2022, which create chain events that can affect house prices and rents. To explore the dynamics of the UK housing market, we introduce an Agent Based Model (ABM) featuring interactions between the mortgage, buy-to-let and rental housing markets. We use the model to understand the effects of interest rate and maximum loan-to-value shocks. The ABM demonstrates the complex associations between such shocks, house prices and rents. It shows that a sudden increase in mortgage interest rates decreases housing prices and steeply increases rent prices within 5 years. It also shows that a sudden decrease of the loan-to-value ratio significantly decreases housing prices. [ABSTRACT FROM AUTHOR]