Missed rents during tenancy of apartments
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Insinööritieteiden korkeakoulu |
Master's thesis
Ask about the availability of the thesis by sending email to the Aalto University Learning Centre oppimiskeskus@aalto.fi
Authors
Date
2017-06-12
Department
Major/Subject
Kiinteistöjohtaminen
Mcode
M3003
Degree programme
Kiinteistötalouden koulutusohjelma
Language
fi
Pages
73
Series
Abstract
In Finland living in an apartment that one owns is common and nationally the built environment is the largest asset class in terms of invested private equity. Residential real estate investing is also very common with most investors owning between one and three investment apartments. One of the key elements of residential investing is tenant selection as the cash flow that an investment apartment generates comes from tenants paying their rents. Tenant selection is especially crucial the fewer apartments an investor owns: if an investor has two apartments and one of them is empty his income is reduced by 50%. If an investor has 1000 apartments and 100 of them are empty, his income is reduced by only 10%. Irrespective of the number of apartments an investor owns however, missed rents constitute a considerable financial loss to the landlord and therefore it is in the investor’s best interest to minimize the number of bad tenants. The literature review concentrated on how investors, real estate consultants, various organizations etc. vet their applicants. The reason for studying how the aforementioned groups compare their applicants is that no studies were found where residential tenants had been directly studied with regard to their tendency to pay their rents on time. When landlords compare potential tenants, financial aspects, the stability and ability of making lease payments is heavily weighed. The empirical part concentrated on a regression analysis of SATO’s tenant information database where people who lived alone were chosen for detailed analysis. Linear and logistic regressions were chosen as the regression methods. Linear regression modeling was done using IBM SPSS and logistic regression was done using Waikato Environment for Knowledge Analysis (WEKA), a program utilizing machine learning. In conclusion no connections were found between the inability to pay rents on time and any of the factors included in the study. It is important to note that the data did not include information such as the financial records of the tenants, nor did it include their criminal records, tendency to use micro financing (pikavippi) or other potentially relevant information on the ability of the individual to manage their personal finances.Description
Supervisor
Viitanen, KaukoThesis advisor
Laitala, AriKeywords
vuokralaisvalinta, tenant selection, regressioanalyysi, regression analysis, asuntosijoittaminen, residential real estate investing