Location Analysis and Pricing of Amenities

Modern location analysis evaluates location attractiveness almost in real time, combining the knowledge of local real estate experts and artificial intelligence. In this paper we develop an algorithm – The Amenities Magnet algorithm – that measures and benchmarks the attractiveness of locations based on the urban amenities’ footprint of the surrounding area, grouped according to relevance for residential purposes and taking distance information from Google and OpenStreetMap into account. As cities are continuously evolving, benchmarking locations’ amenity-wise change of attractiveness over time helps to detect upswing areas and thus supports investment decisions. According to the 15-minute city concept, the welfare of residents is proportional to the amenities accessible within a short walk or bike ride. Measuring individual scorings for the seven basic living needs results in a more detailed, disaggregated location assessment. Based on these insights, an advanced machine learning (ML) algorithm under the Gradient Boosting framework (XGBoost) is adapted to model residential rental prices for the region Greater Manchester, United Kingdom, and achieves an improved predictive power. To extract interpretable results and quantify the contribution of certain amenities to rental prices eXplainable Artificial Intelligence (XAI) methods are used. Tenants' willingness to pay (WTP) for accessibility to amenities varies by type. In Manchester tram stops, bars, schools and the proximity to the city center in particular emerged as relevant value drivers. Even if the results of the case study are not generally applicable, the methodology can be transferred to any market in order to reveal regional patterns.