HOUSING AFFORDABILITY ANALYSIS IN KAZAKHSTAN BASED ON D-HAQ MODEL Cover Image

HOUSING AFFORDABILITY ANALYSIS IN KAZAKHSTAN BASED ON D-HAQ MODEL
HOUSING AFFORDABILITY ANALYSIS IN KAZAKHSTAN BASED ON D-HAQ MODEL

Author(s): Daulet Mukayev, Urpash Zh. Shalbolova, Justyna Tanas
Subject(s): Economy, Socio-Economic Research
Published by: Fundacja Centrum Badań Socjologicznych
Keywords: housing affordability; D-HAQ model; mortgage; down payment affordability; monthly repayment affordability; regions

Summary/Abstract: Housing affordability is an acute socio-economic problem in many countries, and Kazakhstan is no exception. The purpose of the article is to assess the affordability of purchasing houses in the regions of Kazakhstan, taking into account dynamic changes in household income and expenditures. The analysis used data from 17 regions of Kazakhstan for 2021, which were collected from the Bureau of National Statistics, Statistical Bulletin of the National Bank, and analytical web portal. Based on the data, three indicators – down payment affordability (DPA), monthly repayment affordability (MRA), and dynamic indicator TAM, which is the time to achieve adequate MRA – were calculated. Then the Housing Affordability Quadrant was built. The results of the analysis showed that all regions of Kazakhstan are located in the third quadrant. This means that households in these regions have difficulty making both down and monthly payments. Full housing affordability in any part of the country is not observed. Then the affordability of housing under the preferential mortgage lending program was assessed, and as a result, housing affordability improved only slightly. The authors concluded that the lower the solvency of monthly payments, the less effective the effect of increasing affordability by lowering the interest rate on mortgage loans. Instead, it is recommended to take state control of prices in the real estate market in each region of Kazakhstan.

  • Issue Year: 15/2022
  • Issue No: 4
  • Page Range: 28-42
  • Page Count: 15
  • Language: English
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