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Do efficiencies really matter? Analysing the housing finance sector and deriving insights through data envelopment analysis

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  • Santosh Gopalkrishnan
  • Shiba Prasad Mohanty
  • Megha Jaiwani

Abstract

This study aims to assess the efficiency of housing finance companies operating in India by applying Data Envelopment Analysis (DEA). We analyse 26 housing finance companies’ efficiency using various key financial indicators. In addition to DEA, we utilised Tobit regression to investigate the determinants of efficiency in housing finance companies. The findings indicate that large firms need internal restructuring of their coefficients to achieve efficiency, while small firms maintain efficiency within their capacity in the given scenarios. The total factor productivity change for housing finance companies was the highest in 2020–21, followed by a comparative decline in the subsequent year. By considering censored or truncated data, Tobit regression allows us to identify the specific factors influencing efficiency scores derived through DEA. The independent variables used in the Tobit regression model include financial indicators and other relevant factors impacting housing finance companies’ efficiency. Overall, this study sheds light on the performance of housing finance companies and highlights the financial parameters necessary for maintaining a robust non-banking financial system in the Indian economy. The combination of DEA and Tobit regression provides a comprehensive understanding of efficiency and aids in identifying areas for improvement in the housing finance sector while benefiting policymakers and industry stakeholders alike.

Suggested Citation

  • Santosh Gopalkrishnan & Shiba Prasad Mohanty & Megha Jaiwani, 2023. "Do efficiencies really matter? Analysing the housing finance sector and deriving insights through data envelopment analysis," Cogent Economics & Finance, Taylor & Francis Journals, vol. 11(2), pages 2285158-228, October.
  • Handle: RePEc:taf:oaefxx:v:11:y:2023:i:2:p:2285158
    DOI: 10.1080/23322039.2023.2285158
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