Piotr Cwiakowski (p)

Even after brief literature review regarding real estate valuation one can see two crucial problems in the models used in that discipline. The first is the trade-off between interpretability and prediction quality. The other issue is how to deal with spatial autocorrelation in the datasets and still be able to produce out-of-sample forecast (thus the forecast for areas with unknown structure of spatial correlation). In addition to that, that class of models is computationally inefficient and therefore inadequate for mass appraisal. When algorithm has to deal with thousands of points spatial models turn out to be extremely slow. The aim of the paper is to address all three problems, employing a new technique for estimation of spatial structural models. Using bootstrap sampling, unsupervised learning algorithms (Partitioning Around Medoids, PAM) and tessalation we propose new technique for out-of-sample prediction of real estate prices in geo-located point data via traditional spatial microeconometric models. In the first step of the procedure set of spatial models is estimated based on the bootstrapped subsamples from the data. Secondly, PAM is used to choose the most representative model based on its regression coefficients. Subsample which had been used to estimate the selected model is subsequently used to construct representative division of the sample via tessalation. For out-of-sample points, we apply the spatial weights of the polynomial (obtained from tessalation) to which it belongs to predict its value. This procedure solves three problems: i) shrinks size of the dataset while retains its statistical properties (which addresses problem of low capacity of spatial algorithms), ii) allows to estimate stable and representative spatial weights matrix, iii) allows to produce out-of-sample predictions from the structural equation – where impact of each characteristic is relatively easy to understand. Proposed approach will be validated and compared with well-known models in real estate literature – linear regression, kriging/co-kriging and selected Machine Learning algorithms. For estimation we used database which consists 65 674 transactions of apartments from Warsaw secondary real estate market in years 2005 – 2015.

Hajime Seya (p), Masashi Tomari , Makoto Chikaraishi

The present study investigates the impacts of the entry of large-scale retail establishments on the sales and exits of existing local grocery or clothing retail stores in Japan. Similar to related cases in Europe and the US, in Japan as well, the findings of the existing studies are mixed. We examine this topic using micro data from the Japanese Census of Commerce from 1997 to 2014. The difference-in-differences method is used by controlling for the retail establishments’ self-selection bias in terms of location selection by the use of the demand potential function, used in new economic geography literature. The empirical results indicate that the impacts vary based on the periods (short term versus long term), distance from entry locations (distance bands), size of entrants (in terms of floor size), and type of existing stores (grocery or clothing). For clothing stores, the impacts are basically negative on their survival and positive on the sales of surviving stores, especially in the long term. The impacts are stronger when the distance from entry locations is less than 1000 metres. For grocery stores, the tendencies of the impacts are similar to those for clothing stores, but the impacts are estimated to be much weaker. Further, this study provides possible policy implications based on the results.

Aske Egsgaard (p), Lars Pico Geerdsen, Ditte Håkonsson, Ismir Mulalic

Sharing economies defined as digitally mediated systems of exchanging goods and services within an urban area, have become regularly debated topics among technology specialists, economists, policy makers and urban planners. This paper studies the implications of rapid growth of Airbnb on the residential sorting and the housing market in the Copenhagen metropolitan area.
Despite the enormous interest for the impact of Airbnb on cities, the potential impact of a housing sharing system on the households residential sorting and the housing market in general has received little attention. In this paper we focus on the Copenhagen metropolitan area, where we identify the impact of Airbnb by exploiting significant variation in the adoption of Airbnb across city neighbourhoods for the period of nine years. We use all Airbnb listings combined with micro data derived from administrative registers for all households with residence in the Copenhagen metropolitan area distributed over 591 neighbourhoods (zip codes) within the period 2007-2016.
We first carefully describe the evolution of the Airbnb in the Copenhagen neighbourhoods. Then we use detailed housing transaction data to estimate each household’s taste parameter for Airbnb. We show that the mean willingness to pay (wtp) for Airbnb (measured as 1000 tourist-stays in a neighbourhood) is about $550 (DKK 3817). Moreover, estimation results also reveal considerable heterogeneity in this wtp. We find that relatively wealthier households and households with more children are willing to pay more for an apartment in an Airbnb-neighbourhood.
To further analyse Airbnb’s impact on residential sorting, we estimate the likelihood that residents move away from their homes given the exposure to Airbnb in their neighbourhood using a duration model. We find that there is a significant negative correlation between the likelihood to move and the exposure to Airbnb. Again, we find significant heterogeneity between different groups likelihood to move within a given year when living in an Airbnb-neighbourhood. We find that especially younger single parents living in private homes are less likely to move when Airbnb is presence in their neighbourhood.
Our empirical results suggest that Airbnb has a significant impact on the housing market and the residential sorting in the Copenhagen metropolitan area. We show that that demographic differences matter as much as “taste differences” in explaining the demand for Airbnb-neighbourhoods. These findings are important not only to scholars, but also to policy makers, because they may alter the need for regulation of the home sharing services.

Laura Fregolent (p), Matteo Basso , Federica Fava

Globally recognized for its peculiar urban form and the richness of historical and architectural heritage, Venice (Italy) is increasingly affected by mass tourist flows whose pressures on the built environment and the social-ecological structure of the city almost threaten its recognition as a UNESCO World Heritage Site. Over the years, official statistics and researches have confirmed such interpretation, by highlighting the de-population processes affecting the historical city center and its progressive “touristicization”.

Within international policy discourses which intentionally consider creativity, culture and tourism as central tools of urban policy, Venice represents a significant example of the future of many city centers all over the world, when the interaction between exceptional touristic dynamics and ordinary citizens’ lives is not effectively governed. In a city where the housing sector is in itself particularly problematic (for material conditions, supply and costs), public debates increasingly highlight conflicts between the demands of houses for residents, students and city-users and those of tourists.

Given an overview of the recent demographic and socio-economic changes affecting the city (both in its historical center and the peripheries), this paper presents and discusses cases of “spaces of resistance” currently taking place throughout the Municipality, where innovative housing practices are set in motion by different actors. The aim of this paper is to stimulate a broader discussion about the tools that could be used to implement policies and practices for a different use of the city. These ongoing dynamics of housing exclusion and re-appropriation become the starting point of a critical reflection of possible anti-gentification strategies based on alternative ways of producing and regulating the housing sector.

Empirical evidences are the outcome of common researches shared by the authors in the last years, which intentionally mixed quantitative and qualitative methods. Beyond GIS-based quantitative analyses, a selection of case-studies, investigated through techniques such as in-depth interviews and participants observations, will offer an insight into different practices and different social housing providers.