Dr. Ignacio Aravena (p)

I estimate the causal effect of urban violence hotspots on real estate outcomes using the October 2019 social unrest in Santiago, Chile. Combining georeferenced incident hotspots from the national prosecutor’s office with the universe of property transactions, I implement difference-in-differences and event-study designs with block, month, and municipality-by-year fixed effects. Residential prices in blocks with moderate to high violence exposure experience price declines of about 5–6% relative to controls, while low-exposure areas show no detectable effect. These discounts persist and grow over five years and are smallest during the metro-closure phase, when an accessibility mechanism would predict the largest impacts; furthermore, they increase after service resumed and COVID restrictions ended. To separate violence from accessibility disruptions, I classify blocks into mutually exclusive channels (Violence Only, Metro Only, Both, Control) and estimate a triple-difference decomposition. Both approaches indicate that residential price effects are driven by violence exposure rather than proximity to closed stations. Residential transaction volumes do not differentially decline, suggesting adjustment occurs primarily through prices. In contrast, commercial price effects are not significant, but violence exposure substantially reduces the probability of any commercial transaction, consistent with market thinning and potential selection among observed sales. Effects are concentrated in medium- and high-socioeconomic status neighborhoods and in central municipalities, consistent with greater sensitivity to perceived safety risks among higher-income buyers.

Mr Constantin Tielkes (p), Mr. Jonas Röder-Löhr

Urban Economists view cities as monocentric entities centered around a Central Business District (CBD). This paper investigates the slope and the evolution of the price gradient between the CBD and the rest of the settlement area for 1,108 cities in Germany. In our empirical work, we employ a novel definition of CBD that relies on the density and distribution of commercial activity within a city. We find that urban gradients are highly heterogeneous within Germany, with around 27 % of German cities even featuring a negative gradient. We find that around 70 % of the CBD-premium can be attributed to apartment quality, neighborhood characteristics and access to transport infrastructure. CBD-premiums decreased considerably between 2013 and 2016, but have registered an uptick since the Covid pandemic.

Ms Christina Kibasi (p)

What is a housing market area? Is it the area where the demand and supply of houses are at equilibrium? Is it a self contained territory where individuals live and work in the same place, or is it an area whose prices are inelastic?

A great deal of models have been created by real estate professionals, urban planners, academicians, and government agencies on the dimension of using government census data to drive decisions throughout the period. Though models with a 10-year time lag and robustness have been tested for effectiveness, efficiency, and replicability, coping with the pace of science and technology advancements.

Hence, the core purpose of the research is to define and delineate housing market areas using scalable and replicable models of spatial analysis by exploring spatial statistics, examining spatial dependence, and linking with the spatial heterogeneity assumptions, a crucial factor in distributing spatial weights across the Greater Manchester county in north-western England. The global spatial autocorrelation was computed to determine where the area has spatial autocorrelation or values are just random (no spatial dependence). The results of the Global Moran's I depict that there is spatial dependence in the central part of Manchester and positive spatial autocorrelation such that high prices and rents are concentrated in some parts of Trafford with a house price-to-rent ratio above 20, while there is negative spatial autocorrelation in parts of Wigan, Tameside, and Bury, whose house prices are varied and dispersed and whose house price-to-rent ratio is between 17 and 20.

Moreover, LISA (Local Indicators of Spatial Autocorrelation) for more localized analysis depicted the H-H values in the central part of Manchester, Trafford, Bolton, Stockport, and Salford, while L-L values were depicted in Oldham, Bury, Tameside, and Wigan. The places that had spatial outliers (L-H, H-L) were in the Rochdale boroughs whose price demand is elastic.

Therefore, using spatial autocorrelation is a newer advancement than former travel-to-work areas and migration containment rates in global development due to the rise of people working from home such that commuting patterns do not depend on daily transit between home and church. Utilizing the big data available in online property websites at the lowest level of postcode unit makes it easier to create functional boundaries that inform policy efficiently.

Detailed research is needed by adding the time factor to compute spatio-temporal autocorrelation, which vividly influences the real estate sector for effective and timely decision-making.