Automated Real Estate Valuation With Machine Learning: A Case Study on Apartment Sales in Yerevan


  • Henrik Tigran Sergoyan Technical University of Munich
  • Grigor Vahan Bezirganyan Technical University of Munich



Real-estate market, machine learning, automatic valuation, feature importance calculation, XGBoost


Real estate is one of the major sectors of the Armenian economy and has been developing dynamically since Armenia transitions from planned to market economies in early 1990s. More recently, large online platforms have been developed in Armenia to advertise real estate offerings, thus reducing information asymmetry, and increasing liquidity in both sales and rental markets. Simultaneously, granular geospatial data became increasingly affordable via platforms such as OpenStreetMap, Google Maps and Yandex Maps. With granular data concerning a representative portion of the real estate offering available online, it is increasingly tenable to monitor the real estate market in real time and develop analytical tools that can automatically and accurately estimate the value of real estate assets based on their internal and external features. This paper sets out to analyze Armenia real estate market and assess the performance of a special class of machine learning models while predicting the price of a square meter of apartments in Yerevan. Furthermore, it is presented the way to determine the most decisive factors which have an influence on the price of apartments on sale.


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Author Biographies

Henrik Tigran Sergoyan, Technical University of Munich

Germany, Munich) – Technical University of Munich, Mathematics department, Master student

Grigor Vahan Bezirganyan, Technical University of Munich

(Germany, Munich) – Technical University of Munich, Mathematics department, Master student


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How to Cite

Sergoyan, H. T., & Bezirganyan, G. V. (2022). Automated Real Estate Valuation With Machine Learning: A Case Study on Apartment Sales in Yerevan. Journal of Architectural and Engineering Research, 2, 83–91.