Real Estate

Intermediaries are central to complex transactions. In housing markets, real estate brokers coordinate information flows, reduce search costs, and guide lay buyers and sellers through legal and financial steps. Despite this importance, scholarship on brokerage is dispersed across disciplines and methods. This paper presents a semi-systematic review of peer-reviewed articles published between 1970 and 2021. We map (i) study characteristics (country of origin and field), (ii) the distribution of units of analysis (individual, firm/organization, market), and (iii) the most frequently examined topics. Our synthesis indicates steadily rising academic interest but a fragmented knowledge base. We conclude by highlighting gaps—especially the scarcity of cross-country comparisons and the relative lack of qualitative and mixed-method studies on brokers’ practices and experiences.

The housing deficit in developing countries is a common challenge, primarily impacting low-income populations. This paper investigated interinstitutional workflows using Building Information Modelling (BIM) as a social technology to improve the efficiency of design and construction stages in social housing projects. Following a systematic literature review, process maps were developed and applied in a case study within a Brazilian urban community, located in a coastal city with a demographic density of 3602 inhabitants per square kilometre, involving a collaboration framework between a university and municipal authorities. Based on the party’s collaboration and precise cost estimation, the results indicate that this BIM-enabled collaboration supports the governmental decision-making process and leads to more effective resource management and optimised design costs, mainly during the design and construction phases. Therefore, this study concludes that digital modelling workflows are a powerful strategy for developing social housing projects because they facilitate the inclusion of families in the design and decision-making processes. Expanding this approach through integration with geospatial and public agency data is a promising area for future research, using such models in risk assessment policies and city urban planning.

Housing price indices (HPIs) are employed to assess the impact of the business cycle, monetary policy, housing policies, and local market dynamics. However, comparative empirical analysis of different HPI methodologies has not been conducted to measure why or when they may diverge and whether these differences are meaningful. Two leading US HPI choices, the repeat-sale transactional (S&P Case–Shiller) and characteristic-based hedonic (Zillow) indices, although highly correlated, generate different distributions and time-series properties primarily at the city level. The spread between these two HPI choices measures the difference between housing market transaction intensity and a willingness-to-pay characteristic valuation. We find that transactional indices are more volatile, with HPI spreads associated with both macro and local drivers. The transactional index will rise more rapidly in a market with increased buying (positive macro and local market conditions) and fall further in a market with increased selling (negative macro and local market conditions) relative to a hedonic index. A buyer- or seller-biased spread between a transactional and hedonic housing price index (HPI) may impact policy judgments during housing market extremes.