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Pricing Factors Analysis in the Residental Rental Property Market in Moscow

Student: Diumkeeva Daiana

Supervisor: Nataliya Zvezdina

Faculty: Faculty of Economic Sciences

Educational Programme: Economics and Statistics (Bachelor)

Year of Graduation: 2024

Moscow is a center of attraction for people from various regions. It is famous for its rapid pace of development, high incomes and career opportunities. The increased interest in the capital raises the housing issue. The need for housing leads to a rapidly changing structure of supply and demand and, as a consequence, to the growth of housing prices. Moreover, the increase in the key rate, the tense economic situation in the country and rising prices of construction materials exacerbate the situation. Due to the fact, that the problem of high demand for housing in Moscow is becoming more and more acute and urgent, buying an apartment is becoming more difficult, so many people consider renting as an alternative option. In the current situation, the question arises as to what is more profitable: renting housing or buying an apartment in a mortgage. All of the above confirms the relevance of this topic. The purpose of the study is to identify the factors affecting the rental housing market and determine what in the current conditions is more profitable in Moscow: long-term rent or purchase of housing in mortgage. The research sample consists of 6 thousand real estate rental advertisements in Moscow from the real estate portal "CIAN". 18 indicators were identified, presumably affecting the cost of rent. The following results were obtained: 1.Variables responsible for the area of the apartment, time to the subway on foot, kitchen share, number of bathrooms, rooms, air conditioning, elevator, balcony, furniture, underground parking, type of repair, type of house, district, class of housing, possibility to have pets significantly affect the rental price for the whole sample; 2. District and class of housing have the biggest impact on the rental price for the whole sample, for the most expensive and cheapest apartments, as well as for each class separately; 3. Cluster analysis resulted in the optimal division into 2 clusters; 4. Different cases of mortgage and renting were considered.

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