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

Gopalkrishnan, Santosh; Mohanty, Shiba Prasad; Jaiwani, Megha Article Do efficiencies really matter? Analysing the housing finance sector and deriving insights through data envelopment analysis Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Gopalkrishnan, Santosh; Mohanty, Shiba Prasad; Jaiwani, Megha (2023) : Do efficiencies really matter? Analysing the housing finance sector and deriving insights through data envelopment analysis, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 11, Iss. 2, pp. 1-27, https://doi.org/10.1080/23322039.2023.2285158 This Version is available at: https://hdl.handle.net/10419/304278 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Do efficiencies really matter? Analysing the housing finance sector and deriving insights through data envelopment analysis Santosh Gopalkrishnan, Shiba Prasad Mohanty & Megha Jaiwani To cite this article: 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, 11:2, 2285158, DOI: 10.1080/23322039.2023.2285158 To link to this article: https://doi.org/10.1080/23322039.2023.2285158 © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. View supplementary material Published online: 27 Nov 2023. Submit your article to this journal Article views: 780 View related articles View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 FINANCIAL ECONOMICS | RESEARCH ARTICLE Do efficiencies really matter? Analysing the housing finance sector and deriving insights through data envelopment analysis Santosh Gopalkrishnan 1 *, Shiba Prasad Mohanty 1 and Megha Jaiwani 1 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. Subjects: Banking; Credit & Credit Institutions; Risk Management; Economics Keywords: housing finance company; data envelopment analysis; efficiency evaluation; non-banking finance company; performance analysis ABOUT THE AUTHORS Santosh Gopalkrishnan is working as an Associate Professor at Symbiosis Institute of Business Management, Pune. He has completed Ph.D. in the area of Banking. He is having 15 years of rich experience in the field of academia and industry. His broad research interest lies in the field of big data analytics and financial economics. Shiba Prasad Mohanty is a Research Scholar at Symbiosis Institute of Business Management, Pune. He is pursuing Ph.D. in the area of Banking and Finance. Inter alia, his interest lies in the field of financial astrology, financial economics, shadow banking and corporate governance. Megha Jaiwani is a Junior Research Fellow at Symbiosis Institute of Business Management, Pune. Her current research focuses on financial stability, systemic risk, and the impact of banking regulations on financial intermediation. She is dedicated to continuing her research and is passionate about finding solutions to financial resilience challenges. Gopalkrishnan et al., Cogent Economics & Finance (2023), 11: 2285158 https://doi.org/10.1080/23322039.2023.2285158 Page 1 of 27 Received: 30 March 2023 Accepted: 14 November 2023 *Corresponding author: Santosh Gopalkrishnan, Symbiosis Institute of Business Management, Deemed University, Pune, Symbiosis International, Pune 412115, India E-mail: [email protected] Reviewing editor: David McMillan, University of Stirling, United kingdom Additional information is available at the end of the article © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. 1. Introduction The efficient functioning of financial institutions in the economy indicates an optimistic signal for growth and development in the market. In a developing economy like India, non-banking financial companies (NBFCs) predominantly play a vital role vis-à-vis commercial banks towards financial intermediation by catering financial services to the last mile. NBFCs have had a somewhat unpredictable presence, survival, and growth in the Indian financial landscape since the 1960s, which are invariably named “shadow banks” or “non-banking financial intermediaries”. These institutions are not banks but bank-like financial companies. They are financial intermediaries accepting deposits, delivering credit, and channelling scarce financial resources to industries or retail customers (Zhang et al., 2023). They substitute banks in meeting the financial needs of corporates, delivering credit to the unorganised sector and local borrowers (Acharya et al., 2013). The nature of the business of shadow banks differs from country to country—they are hard to define uniformly from a global perspective. To overcome this ambiguity, the Financial Stability Board (FSB, 2011) defines shadow banking as “A credit intermediation system involving entities and activities outside the banking system.” The FSB definition seems to have become most acceptable in academic research. In global finance, shadow banks first gained the limelight at par with commercial banks during the global financial crisis of 2007–08. The financial crisis resulted from market misconduct and inadequate regulation (Adrian & Ashcraft, 2012). Shadow banks significantly created US housing bubbles by participating in banking activities such as credit intermediation, liquidity, maturity transformation, and subprime lending, as they usually did not have direct access to central bank lending and eventually experienced runs. The systemic risk of the NBFCs could not be underrated as the risk propagates among the formal financial system due to the interlinkages among the commercial banks; hence, early identification and quick policy intervention could stop the spillover in the early stage (Ghosh & Mazumder, 2023). As a reminder of that crisis, the shadow banking system in India has come across a perfect storm in recent times in the housing market. With the significant growth of the shadow banks in the economy after the GFC in 2007–08, the Reserve Bank of India (RBI) in 2011, under the leadership of Ms. Usha Thorat, submitted a report by segregating the NBFCs into several categories based on their nature of operations and business model. They were classified as; Deposit and non-deposit taking, Asset finance companies, Investment companies, Loan companies, Microfinance institutions, Infrastructure finance companies, Asset reconstruction companies, Housing finance companies, and peer-to-peer lending companies. Housing finance companies (HFCs) stand out as peculiar among the various categories of NBFCs due to their innovative business models, concentration in the niche market, and substitute service providers to commercial banks. Including the fact that it is the second-largest borrower of funds from the financial system, with a gross payable of around H7.40 lakh crore, compared to H12.46 lakh crore for the NBFC sector as a whole as of March 2022 (RBI, 2021). The recent collapse of leading housing finance companies such as; Dewan Housing Finance Ltd. (DHFL) and Reliance Capital in 2018 posed a systemic risk to the allied sectors of the economy (Chandrasekhar, 2020). Initially, the crisis started in the housing market and slowly aggravated real estate and infrastructure financing, as the sectors are closely interconnected. The collapse of Infrastructure and Leasing Financial Services Ltd (IL&FS) and Srei Infrastructure Ltd is a testament to the crisis. That could be related to governance failure, asset liability mismatch or liquidity mismanagement. When the housing market started reviving after the liquidity crisis, the COVID-19 pandemic hit the market badly, followed by the regulators’ over-imposition of the prudential norms. In 2019, the regulatory shifting of HFCs happened from the National Housing Bank (NHB) to RBI, which put the housing companies in trouble, as the regulator is trying to manage the HFCs like the commercial banks. Among the emerging economies, shadow banks in India have registered exaggerated growth as these entities operate outside the regulatory purview; thus, their activities lead to the system’s fragility of the overall financial system (Bhattacharjee & Pati, 2022). Therefore, their efficiency evaluation under a regulated environment is equally important as banks in India, as the HFCs have to prudentially adhere to the regulatory norms imposed by the RBI from time to time. Gopalkrishnan et al., Cogent Economics & Finance (2023), 11: 2285158 https://doi.org/10.1080/23322039.2023.2285158 Page 2 of 27 The literature on the efficiency measurement of NBFCs, particularly HFCs, is scarce (Dutta et al., 2020; Sufian, 2007). Much of the shadow banking literature also highlighted the paucity of research in developing countries due to the unavailability of the data, which created enthusiasm and eagerness among the researchers to examine this sector from different angles. The interconnectedness of the shadow banks with the commercial banks could be traced in the early stage with the help of network-based measures as crises are becoming frequent as well as the contagion of risk is becoming prominent due to the dense linkages among the financial institutions (Chaturvedi & Singh, 2022).HFCs, especially among the NBFCs categories, are gaining market attention for various reasons; as the sector is financed through capital market instruments and also highly interconnected by way of borrowings from commercial banks, their failures will amplify the systemic risk to the formal financial system of the country (“Ahmad et al., 2019”; “FSB, 2011”; “Pozsar, 2008”; “Sinha, 2013”). It is popularly called “Money market funding of capital market lending” (Mehrling et al., 2013). Hence this study has considered various determinants to examine the efficiency of the HFCs in India, as all the HFCs are regulated by the RBI irrespective of their asset size. 1 This study is showcased through the following sections in this manuscript. The initial section begins by explaining the Indian housing finance sector in the current landscape, followed by an organised literature review that provides an in-detailed observation of the efficiency analysis. The fourth section highlights the rationale for considering DEA on HFCs, followed by the detailed methodology in the fifth section. This is followed by an empirical analysis and findings in the sixth section, and the seventh section presents the conclusion along with implications, limitations and future directions for this study. 2. Housing finance companies in the current landscape With credit growth outpacing commercial banks, NBFCs have emerged as key players in the financial sector. Customers were pulled to HFCs due to their well-developed collection system, quick decision-making process, and quick service response. HFCs were willing to take on additional risks and lend to small and medium-sized enterprises and other businesses that banks could not lend to due to creditworthiness issues in exchange for a marginally higher return. Commercial banks have been making home loans to their customers since the early 2000s. The market became more competitive when they intensively forayed into this industry by creating separate housing finance verticals to provide services in India. However, the entry of private players in the housing space enhanced the competition within the industry. As of 31 March 2021, we have 100 + active companies on the land to provide services in the market (NHB, 2021). As mentioned in Table 1, several leading commercial banks sponsor 06, and few are backed by strong parental holding, which creates a distinction among the players when confronting a sectoral crisis. In addition, the HFCs are also classified as deposit-taking and non-deposit-taking. Depending on the intensity of the interconnectedness with the formal financial system, efficiency measurement is the need of the hour for providing a holistic solution to the market. In 2018, the HFCs faced several storms due to the chronological failure of several leading housing companies in India. The immediate collapse of one after another put numerous questions upon their efficiency, as other players in the market also faced the hit because of the crisis. The collapse of the leading infrastructure finance company Infrastructure and Leasing Financial Services Ltd. (IL&FS) posed a systemic risk to the market as it dealt with many leading infra projects in the country with the help of 350+ subsidiaries. In continuation, two leading housing finance companies, DHFL and Reliance Capital, failed because of liquidity issues and mismanagement in the company’s corporate governance. Followed by the chaos in the market, in 2021, a Kolkata-based age-old equipment finance company, Srei Infrastructure Ltd., went to a company law tribunal for liquidation. Moreover, the housing finance sector in India is characterized by a dynamic environment where companies frequently undergo mergers or acquisitions. This industry-wide phenomenon results from strategic decisions, regulatory changes, and economic factors (Bhanot et al., 2020). The housing industry has come under scrutiny in the wake of recent crises and the failures of prominent housing companies. This has prompted regulators and academics to Gopalkrishnan et al., Cogent Economics & Finance (2023), 11: 2285158 https://doi.org/10.1080/23322039.2023.2285158 Page 3 of 27 Table 1. No. Of HFCs registered under NHB 2012–13 2013–14 2014–15 2015–16 2016–17 2017–18 2018–19 2019–20 2020–21 Public Limited Companies 48 50 55 63 66 73 79 77 80 Private Limited Companies 8 8 9 8 17 18 20 24 22 Source: National Housing Bank (NHB). Gopalkrishnan et al., Cogent Economics & Finance (2023), 11: 2285158 https://doi.org/10.1080/23322039.2023.2285158 Page 4 of 27 focus on assessing the sector’s efficiency. The current study seeks to contribute to this discourse by providing empirical evidence, with a specific emphasis on Housing Finance Companies (HFCs) due to their pivotal role in shaping the housing market and its implications for effective regulatory strategies. 3. Literature review 3.1. Efficiency measurement approaches across the globe Efficiency measurement in the context of global financial institutions has been a subject of study for many years. Researchers have employed various techniques to assess the efficiency of these institutions, each with its own evolution and limitations. Originally, ratio analysis was the go-to method for evaluating short-term operating performance. However, as time went on, the flaws in ratio analysis became evident, leading to misleading and incomplete results (Chen & Ray, 2010; Quaranta et al., 2018). In response to the limitations of ratio analysis, two prominent techniques gained prominence in academia: the stochastic frontier approach (SFA) and data envelopment Table 2. List of Housing Finance companies considered for the study Sl. No. Name of the HFCs Large size firm 1 Housing Development Finance Corporation. Ltd. 2 LIC Housing Finance Ltd. 3 Indiabulls Housing Finance Ltd. 4 Piramal Capital & Housing Finance Ltd. 5 PNB Housing Finance Ltd. 6 Tata Capital Housing Finance Ltd. 7 Can Fin Homes Ltd. 8 Reliance Home Finance Ltd. 9 IIFL Home Finance Ltd. 10 ICICI Home Finance Co. Ltd. 11 Aadhar Housing Finance Ltd. 12 GIC Housing Finance Ltd. 13 Repco Home Finance Ltd. 14 Sundaram Home Finance Ltd. 15 Mahindra Rural Housing Finance Ltd. 16 Edelweiss Housing Finance Ltd. 17 Poonawalla Housing Finance Ltd. 18 Cent Bank Home Finance Ltd. Small size firm 1 L & T Housing Finance Ltd. 2 Aptus Value Housing Finance India Ltd. 3 Shriram Housing Finance Ltd. 4 Vastu Housing Finance Corporation Ltd. 5 India Shelter Finance Corporation Ltd. 6 Shubham Housing Development. Finance Co. Ltd. 7 Muthoot Housing Finance Co. Ltd. 8 Svatantra Micro Housing Finance Corporation Ltd. Source:National Housing Bank. Note: L&T Housing Finance Ltd. merged in the year 2020. Other HFCs are not considered for the study because of the large missing data witnessed during the compilation of the data frame. The data has been collected from the Prowess database provided by the CMIE. Gopalkrishnan et al., Cogent Economics & Finance (2023), 11: 2285158 https://doi.org/10.1080/23322039.2023.2285158 Page 5 of 27 analysis (DEA). SFA, while powerful, is less prevalent in the literature due to its complexity, involving parametric methods and linear programming techniques to address efficiency issues. Conversely, DEA became popular due to its versatility in incorporating multiple inputs and outputs into the model, making it the most frequently used technique in efficiency studies across various disciplines. One of the foundational models in DEA, known as the CCR model, was initially developed by Charnes et al. in 1978 and subsequently refined by Banker et al. in (Banker et al., 1984). This model introduced the concept of “decision-making units” (DMUs) and found significant application in studies related to the banking sector (Liu et al., 2013). Over time, researchers have introduced a plethora of advanced techniques, including Bootstrap DEA, DEA-TOPSIS, modified Bootstrap DEA, super-efficiency DEA, Malmquist Productivity Index, Double Bootstrap Truncated, Dynamic Network DEA, Meta Frontier DEA, Integrated DEA-TOPSIS, Two-stage network DEA, and Dynamic Slackbased DEA, among others. These techniques have been employed to assess the efficiencies of financial entities on a global scale. However, these methods come with several issues, and one of the most significant limitations that researchers are increasingly recognizing is the problem of endogeneity (Orme & Smith, 1996). It’s essential to highlight that, despite researchers being aware of the potential endogeneity issue in DEA studies; they have not yet reached a consensus on a definitive solution. Despite various efforts to address endogeneity concerns in the banking industry (Antunes et al., 2022; Subhash & Chen, 2010; Tan et al., 2021; Xiaogang et al., 2005), particularly in the context of second-stage regression analysis, a comprehensive resolution remains elusive. Mayston (2017) attempted to develop a DEA model to address this issue, but as of now, no study has undertaken an in-depth examination of endogeneity within the production process. Consequently, researchers often continue to rely on traditional DEA approaches, recognizing the potential problem of endogeneity but lacking a universally accepted solution. This highlights the ongoing challenge of addressing endogeneity in efficiency analysis within the financial sector. 3.2. Efficiency studies on finance across the globe Several researchers have studied the DEA application in various sectors across the globe. As our study is limited to the NBFCs, we have listed the seminal studies in banking and finance, as NBFCs are similar to the banking model in terms of operations and other service-provided criteria. When it comes to banking sector studies with the help of DEA, it is primarily researched due to the availability of the data and the feasible fitness of the input-output model. There are seminal studies conducted (Das & Ghosh, 2006; Jagwani, 2012; Raina & Kumar Sharma, 2013; Saha & Ravisankar, 2000; Sanjeev, 2006, 2009; Singh et al., 2020; Tandon et al., 2014) on the efficiency measurement of the group of banks in India (public sector, private sector, foreign banks) by considering the ownership allocation, non-performing assets, firm size, branch allocations and the staff management with the help of DEA. Kumar and Gulati (2008) conducted a study on the application of the DEA on 27 public sector banks of India from the year 2004–05 by way of using a two-stage DEA technique; they divided the efficiency scores into overall technical, pure technical, and scale efficiency. After obtaining the OTE scores in the first stage, they regressed with the environmental variables in the second stage. Kaur and Gupta (2015) used DEA methodology to examine the performance of the Indian banking sector and concluded that the state bank group appears to be the most efficient, followed by private and nationalised banks. The productive efficiency of Indian banks increased during the study period, but the increase was not consistent in the subsequent years. They have concluded that bank efficiency did not project any benefits to the economies of scale and independent of the bank’s size. Gulati and Kumar (2017) used two-stage DEA techniques followed by a bootstrapped truncated regression algorithm to control the exogenous variables to measure the intermediate and operating efficiencies of public and private sector banks. They found that public sector banks struggled to generate income, entailing an overhaul of their traditional and non-traditional activities to Gopalkrishnan et al., Cogent Economics & Finance (2023), 11: 2285158 https://doi.org/10.1080/23322039.2023.2285158 Page 6 of 27 generate more income. Their regression results project that the bank size and liquidity position affect intermediation efficiency, profitability, and income diversification. Another group of studies was conducted on the efficiency based on the purely technical, overall technical, and scale efficiency of the group of banks in India. Mainly, managerial efficiency and bank income measures have been studied through the DEA application (Goyal et al., 2018; Kumar et al., 2016; Mehta et al., 2019; Sathye, 2003) and are also quite popular in the banking space. As time moved on, some studies became visible (Hafsal et al., 2020; Kumar & Kar, 2022; Patra et al., 2023; Puri & Yadav, 2013) considering the interest and non-interest income, profit factor and risk capacities of the banks into account became wide popular as the other techniques of the DEA such as two-stage data envelopment analysis, dynamic slack-based DEA, DEA-TOPSIS etc. 2 There are studies available in the global banking arena with the application of the DEA. The research started primarily after the global financial crisis era to measure the efficiency of global banks. The regulatory environment changes and optimising cost efficiency (Avkiran, 2006; Dar et al., 2021; Defung et al., 2016; Dong et al., 2014; Drake & Hall, 2003; Fernandes et al., 2018; Holod & Lewis, 2011; Jaffry et al., 2013; Li, 2020; Liu et al., 2013; Pasiouras, 2008; Stewart et al., 2016; Sufian, 2006; Sufian, 2008, 2011; Sufian & Habibullah, 2009; Sufian & Majid, 2007; Yu et al., 2019) experimented the application of the DEA on their respective countries. This implies the wide acceptability of efficiency studies in the banking domain in the respective countries. 3 Following the banking studies with the application of DEA in domestic and global, there are studies available in insurance and stock markets. According to Ilyas and Rajasekaran (2019), the Indian non-life 4 insurance sector is moderately technical, with room for further development. As a result, all insurers, regardless of size or ownership type, are experiencing increasing returns to scale. Efficiency has a statistically significant negative relationship with size and reinsurance. Sinha (2015) has examined insurance companies’ efficiency with bootstrap DEA’s help and suggested that they are highly efficient in performing in the market. In their study, Mandal and Dastidar (2014) showed that the global slowdown has severely affected the private sector companies, whereas the public sector companies are impacted relatively less in performance levels. Ghosh and Dey (2018) and Jothimani et al. (2017), with the help of a super-efficiency model, have measured the performance of the general insurance companies. Hence, this justifies the vast presence of DEA applications in India’s banking and allied areas and globally. 5 3.3. Efficiency studies on NBFCs in India Few seminal studies have been conducted on the DEA application of NBFCs in India. Several researchers have conducted a handful of studies on various categories of NBFCs, such as; microfinance institutions (NBFC-MFI), on various dimensions to capture the performances and efficiencies in India. Whereas, studies on deposit-taking NBFCs, housing finance companies, asset reconstruction companies, and infrastructure companies’ efficiency measurement have never been examined empirically in the Indian context, as evident from the past studies vis-à-vis commercial banks in India. Dutta et al. (2020) used a two-stage data envelopment analysis to examine the performance of non-banking finance companies using the efficiency model. According to the findings from the research, managers should not solely rely on ROE as an indicator of efficiency but should also consider ROA and income diversity. Bhattacharjee and Pati (2021) examined the non-deposit- taking NBFCs and concluded that inefficient firms required to improve their economies of scale by way of achieving managerial efficiency, as well as the small shadow banks should scale up their business operations by way of improving the asset quality to achieve the efficient frontier. There are pretty reasonable studies on the several applications of DEA on the efficiency measurements of the microfinance institutions evidenced in the Indian context, as MFIs are regulated under the umbrella of the NBFCs in India. Many researchers have tried to examine several nuances of performance measurement and their better improvement for the financial landscape. DEA Gopalkrishnan et al., Cogent Economics & Finance (2023), 11: 2285158 https://doi.org/10.1080/23322039.2023.2285158 Page 7 of 27 of 18 firms are above the productivity score of 1. However, we have witnessed an upsurge in the case of efficiency change (effch), where eight firms perform above the productivity score of 1, while the remaining ten firms are under the score of 1. Further, we have analysed the decomposition of the effch into pure efficiency change (pech) and scale efficiency change (sech) to analyse their aggregate performance in detail. When it comes to pech, it is observed that all 18 firms are performing strictly at the efficiency level, that is, 1. The results of the sech changed completely and observed that only eight firms have a productivity level of either one or more than 1. Table 9 provides a detailed analysis of the changes in productivity over time, followed by Table 10 for the large and small-size HFCs, respectively. The analysis revealed that the total factor productivity (tfpch) has increased by 12.3%. The annual change of tfpch was highest in 2020–21, whereas it was comparatively lesser in the remaining years except in 2021. When it comes to the effch of the large-size HFCs, the mean was 0.990, which indicates that all the firms are functioning at a high-efficiency level. When we analysed the effch of the small-size firms, the situation changed, showing a mean of 0.949, which is comparatively less than that of large firms. Thus, it concludes that, in the case of effch, the large-size HFCs perform comparatively better than the small-size firms in the given capacity. When it comes to the tfpch, the scenario got changed. The small-size HFCs productivity change over the years is 34.9 %, higher than the productivity change shown by large-size firms. 6.2. Stage II: Tobit regression approach In the next step of our empirical analysis, we employed the Tobit regression model to investigate the relationship between “bank-specific” variables and their efficiency. This choice was informed by a critique of the traditional DEA approach, which faces challenges in drawing statistical inferences. Fare and Grosskopf (1996) proposed a two-stage analytical approach to address this issue. In the first stage, DEA is used to calculate efficiency scores, while in the second stage; regression analysis is applied to explain these efficiency scores. Table 5. Overall relative efficiency scores of the HFCs (small size firm) Name of the HFCs CRS Efficiency (OTE) VRS Efficiency (PTE) Scale efficiency (OTE/PTE) Returns to scale L & T Housing Finance Ltd. 0.289 1.000 0.289 Decreasing Aptus Value Housing Finance India Ltd. 0.698 1.000 0.698 Decreasing Shriram Housing Finance Ltd. 1.000 1.000 1.000 Constant Vastu Housing Finance Corp. Ltd. 1.000 1.000 1.000 Constant India Shelter Finance Corp. Ltd. 1.000 1.000 1.000 Constant Shubham Housing Dev. Finance Co. Ltd. 1.000 1.000 1.000 Constant Muthoot Housing Finance Co. Ltd. 1.000 1.000 1.000 Constant Svatantra Micro Housing Finance Corp. Ltd. 1.000 1.000 1.000 Constant Source: Author’s calculation. Gopalkrishnan et al., Cogent Economics & Finance (2023), 11: 2285158 https://doi.org/10.1080/23322039.2023.2285158 Page 14 of 27 However, a notable concern arises from the fact that efficiency scores are censored. To accommodate this aspect, we opted for the Tobit regression model, which can handle truncated data and work with both continuous and categorical variables (Dutta et al., 2020; Singh et al., 2020; Tandon et al., 2014). The choice of the Tobit model is particularly relevant as it accounts for the distribution characteristics of efficiency measures and, in turn, offers valuable insights for policy formulation. Given that inefficiency scores fall within the range of 0 to 1, a Tobit model with two-sided censoring represents a suitable theoretical specification (Das & Ghosh, 2006). Housing finance companies in India exhibit diverse lending practices, leading to variations in operational efficiency. Without addressing endogeneity, these disparities could introduce bias in efficiency scores, underscoring the necessity of employing Tobit regression. Table 7. Malmquist index summary of HFCs (large size firm) DMU ID effch techch pech sech tfpch 1 1.078 0.907 1.000 1.078 0.977 2 0.998 0.883 1.000 0.998 0.881 3 0.799 0.888 1.000 0.799 0.709 4 1.446 1.051 1.000 1.446 1.520 5 0.936 0.860 1.000 0.936 0.805 6 0.883 0.917 1.000 0.883 0.810 7 0.953 0.862 1.000 0.953 0.822 8 1.247 0.824 1.000 1.247 1.027 9 0.757 0.855 1.000 0.757 0.647 10 1.016 0.917 1.000 1.016 0.931 11 1.031 0.853 1.000 1.031 0.879 12 1.023 0.874 1.000 1.023 0.894 13 0.992 0.877 1.000 0.992 0.871 14 1.063 0.886 1.000 1.063 0.942 15 0.893 0.851 1.000 0.893 0.760 16 0.902 0.840 1.000 0.902 0.757 17 0.995 0.875 1.000 0.995 0.871 18 1.000 0.860 1.000 1.000 0.860 Mean 0.990 0.881 1.000 0.990 0.872 Source: Author’s calculation. Table 6. Summarised statistics of efficiency scores Efficiency scores OTE PTE Scale Large size firm E < 0.9 13 0 13 0.9 < E < 1 1 0 1 E = 1 4 18 4 Small size firm E < 0.9 2 0 2 0.9 < E < 1 0 0 0 E = 1 6 8 6 Source: Author’s calculation. Gopalkrishnan et al., Cogent Economics & Finance (2023), 11: 2285158 https://doi.org/10.1080/23322039.2023.2285158 Page 15 of 27 Moreover, the Indian real estate market is subject to economic fluctuations and regulatory changes, which need to be considered when assessing efficiency. Ignoring these external factors could result in endogeneity issues, further justifying the use of Tobit regression to account for their impact. Additionally, data on housing finance companies may be incomplete or contain gaps due to reporting inconsistencies or lack of transparency. The incompleteness of this data can introduce endogeneity, making Tobit regression an effective tool for handling censored and incomplete data. In the housing finance sector, risk assessment often involves complex, unobservable factors that can influence efficiency. The presence of these factors can give rise to endogeneity concerns, which Tobit regression is well-equipped to address through the incorporation of additional controls. Furthermore, given the dynamic nature of the Indian real estate market and the adaptive strategies of companies in response to changing conditions, failing to account for these evolving market conditions could lead to biased DEA results. Lastly, the absence of a universally robust methodology for addressing endogeneity in the context of DEA has led researchers to turn to Tobit regression, a well-established econometric technique, as a pragmatic solution given the complexities of the dataset. Since efficiency scores measured by DEA are censored and constrained within the range of 0 to 1, Tobit regression becomes a necessary method to obtain unbiased results (Eyceyurt et al., 2017; Sufian & Habibullah, 2009). This study employed the Tobit regression method to address this challenge effectively. The fundamental model is as follows. Table 8. Malmquist index summary of HFCs (small size firm) DMU ID effch techch pech sech tfpch 1 0.904 0.853 1.000 0.904 0.771 2 0.933 0.737 1.000 0.933 0.688 3 0.930 0.676 1.000 0.930 0.629 4 1.000 0.708 1.000 1.000 0.708 5 0.941 0.570 1.000 0.941 0.537 6 0.911 0.606 1.000 0.911 0.553 7 0.979 0.691 1.000 0.979 0.676 8 1.000 0.677 1.000 1.000 0.677 Mean 0.949 0.685 1.000 0.949 0.651 Source: Author’s calculation. Table 9. Malmquist index summary of annual means (large size firm) Year effch techch pech sech tfpch (2012–13) 0.748 0.978 1.000 0.748 0.732 (2013–14) 0.987 0.781 1.000 0.987 0.771 (2014–15) 1.196 0.652 1.000 1.196 0.780 (2015–16) 1.034 0.755 1.000 1.034 0.780 (2016–17) 0.919 0.899 1.000 0.919 0.826 (2017–18) 0.839 1.063 1.000 0.839 0.892 (2018–19) 0.916 0.901 1.000 0.916 0.825 (2019–20) 1.050 0.850 1.000 1.050 0.892 (2020–21) 1.350 1.161 1.000 1.350 1.567 Mean 0.990 0.881 1.000 0.990 0.872 Source: Author’s calculation. Gopalkrishnan et al., Cogent Economics & Finance (2023), 11: 2285158 https://doi.org/10.1080/23322039.2023.2285158 Page 16 of 27 Where yi = 0 otherwise, β which presents the set of parameters to be estimated, yi and y� i depicts the observed DEA efficiency scores and zi and β are the set of explanatory variables and their coefficients. In the current study, we have considered the DEA-obtained efficiency scores as dependent variables and several peculiar bank-specific variables as independent variables. As most of the existing studies have considered commercial banks factors in their empirical model, hence, by keeping on view as a base with the earlier studies, we have constructed the basic regression model as follows: In the above regression model coefficients of the independent variables presented by β1to β10(Table 11) and εij are the error terms mentioned in the model to capture the disturbance in the model. A detailed description of the variables and their notations used in the study are mentioned in Table 11. The descriptive statistics of the small and large firms have been depicted in Tables 12 and 14. In the current study, to describe the firm’s characteristics regression model is used. The study has undertaken the efficiency scores obtained from the DEA analysis with the help of the BCC model and then correlated with the firm-specific variables to define the status of the concerned firms. (Tables 12 and 14). Our study initially aligned with previous literature, suggesting a positive relationship between firm size and efficiency observed in larger and smaller firms. To investigate this further, we conducted regression analysis on small-size firms, utilising several firm-specific variables considered in the study (as shown in Table 13). Surprisingly, our results contradicted the earlier findings, revealing that while size positively influences efficiency for larger firms, it exhibits a damaging relationship for small firms. The unexpected negative relationship between firm size and efficiency for small firms could be attributed to various factors. Smaller firms may face challenges with economies of scale, limited resources, and adaptability to market changes. Their specialisation in niche markets may limit growth opportunities, and they might be more exposed to risks than larger, diversified firms. Moreover, smaller firms may lack bargaining power, and managerial capabilities could influence their efficiency. Compliance costs may also impact their performance. Table 10. Malmquist index summary of annual means (small size firm) Year effch techch pech sech tfpch (2012–13) 0.773 0.598 1.000 0.773 0.462 (2013–14) 0.477 1.202 1.000 0.477 0.573 (2014–15) 0.477 1.127 1.000 0.477 0.504 (2015–16) 1.383 0.550 1.000 1.383 0.760 (2016–17) 1.827 0.350 1.000 1.827 0.639 (2017–18) 1.356 0.476 1.000 1.356 0.646 (2018–19) 1.117 0.635 1.000 1.117 0.709 (2019–20) 0.939 0.876 1.000 0.939 0.823 (2020–21) 1.058 0.808 1.000 1.058 0.855 Mean 0.949 0.685 1.000 0.949 0.651 Source: Author’s calculation Gopalkrishnan et al., Cogent Economics & Finance (2023), 11: 2285158 https://doi.org/10.1080/23322039.2023.2285158 Page 17 of 27 The CRISK, considered a peculiar factor for the performance of the firms, resulted as significant but negatively related to firm efficiency. This signifies that increased credit risk reduced the firm efficiency and vice-versa. The findings highlight the increased costs and challenges associated with the firm’s higher credit risk, such as provisions for potential loan defaults, additional risk management measures, and intensified monitoring efforts. As credit risk rises, the firm may allocate more resources to tackle these risks, potentially diverting attention and funds away from core business operations, thus impacting overall efficiency. Another key indicator, Operating Expenses (OPEX), is also found to have a significant and negative relationship with efficiency. This suggests that as operating expenses increase, firm efficiency tends to decrease. The rationale behind this is that higher operating expenses could signal inefficiencies in the firm’s cost structure, resource allocation, or operational processes. It may indicate suboptimal utilisation of resources, inefficient production methods, or increased overhead costs. As a result, the firm’s overall performance and profitability might suffer, making it essential for management to manage and streamline operating expenses to enhance overall efficiency carefully. NIM and ROA, considered an indicator of measuring managerial efficiency, are negatively related to the efficiency of the firms. The results contradict the previous studies (Sufian, 2008; Sufian & Habibullah 2009), where as the results supported the studies conducted (Bhattacharjee & Pati, 2021). Usually, a significant and positive relationship between ROA and NIM in this model better suggests an efficiency mechanism for small firms. A higher ratio predicts better performance, which is in evidence in the current study. CUR and LIR measure liquidity and determine the firm’s ability to meet short-term obligations, negatively influencing the efficiency of small-size firms. It can be concluded that liquidity mismanagement affects the firm efficiency on a large scale. CRAR, the indicator of the firm’s stability to mitigate the shock, negatively impacted the efficiency, which is expected to be positive for better financial strengthening. The scenario changed when it came to large-size firms. Size positively influences efficiency, which was reversed in the case of small firms. It indicates that large-size firms will perform Table 11. Definitions, measurements and notations of the variables Determinants Variable Description of the variables Notation Dependent Variable Efficiency The efficiency score of the firms Eff_score Independent Variables Firm size Natural log of total assets FS Risk efficiency Total loans to total assets CRISK Operating efficiency Operating expenses to total income OPEX Capital size Natural log of the total capital of firms CAP Net interest margin (Interest incomeinterest exp)/total assets NIM Profitability Return on assets ROA Cost of capital Non-interest expenses to total assets POC Financial soundness Current assets to current liabilities CUR Liquidity Liquid assets to current liabilities LIR Firm Stability Capital to risk assets ratio CRAR Source: Author’s calculation. Gopalkrishnan et al., Cogent Economics & Finance (2023), 11: 2285158 https://doi.org/10.1080/23322039.2023.2285158 Page 18 of 27 efficiently with the increase in total asset size. CRISK, the indicator of risk efficiency, negatively influences the firm efficiency, which means more credit disbursed drives the firm efficiency downward. Hence, HFCs should avoid prudent credit sanctioning. OPEX indicates that the firms’ operating efficiency is negatively influenced by their overall efficiency, but it is becoming significant. The negative relationship between ROA and efficiency scores suggests that firms with higher profitability, as measured by ROA, may not necessarily exhibit superior overall efficiency. This finding contradicts the conventional belief that higher profitability always indicates better efficiency. It may indicate that firms prioritising short-term profits through various means, such as cost-cutting measures or taking on riskier assets, might compromise their long-term operational efficiency and sustainability. Similarly, the negative relationship between NIM and efficiency implies that firms with higher net interest margins, potentially generated from charging higher interest rates on loans, may face reduced overall efficiency. This result could be attributed to the possibility that focusing primarily on maximising interest income might lead to neglecting operational efficiency and risk management, ultimately affecting the firm’s overall performance. Table 13. Tobit regression model (small size firm) Eff_score Coef. Std. Err. t P>|t| [95% Conf. Interval] FS 0.00003 0.00003 1.13 0.26 −0.000024 CRISK −6.87042 3.26043 −2.11 0.039 −13.373140 OPEX −0.80874 0.34448 −2.35 0.022 −1.495784 CAP 0.00075 0.00040 1.87 0.066 −0.000051 NIM −67.49316 23.63134 −2.86 0.006 −114.624400 ROA 16.86066 11.66525 1.45 0.153 −6.404950 POC 7.57374 14.12226 0.54 0.593 −20.592230 CUR −0.07619 0.03750 −2.03 0.046 −0.150975 LIR 0.32693 0.18143 1.8 0.076 −0.034923 CRAR 0.00256 0.00652 0.39 0.696 −0.010444 Source: Author’s calculation. Note: LR chi 2 (10) = 66.01; Prob > chi 2 = 0.00; Pseudo R 2 = 0.599. Table 12. Descriptive statistics of variables (small size firm) Variable Mean Std. Dev Min Max Eff_score 0.873375 0.2435117 0.289 1 FS 18154.33 32131.19 110.2 152586.1 CRISK 0.8513329 0.1044164 0.361939 0.9866365 OPEX 0.9878942 1.041096 0.432706 8.295455 CAP 863.8813 1041.729 11.9 5184.6 NIM −0.0612593 0.9686446 −8.59165 0.0997594 ROA 0.1168522 0.0306176 0.033405 0.1817356 POC 0.1611627 0.9587321 0.000599 8.606171 CUR 6.256077 13.49393 0.047264 82 LIR 2.13812 9.441175 0.000874 82 CRAR 69.42688 60.44908 13.28 455.26 Note: Author’s calculation. Gopalkrishnan et al., Cogent Economics & Finance (2023), 11: 2285158 https://doi.org/10.1080/23322039.2023.2285158 Page 19 of 27 These counterintuitive findings warrant further investigation into the underlying factors contributing to the observed negative relationships. Potential reasons could include the firm’s risk appetite, resource allocation strategies, managerial decisions, or market conditions. Understanding these dynamics can help firms balance profitability and efficiency better, optimising their performance in both aspects. Additionally, CUR and LIR considered an indicator of liquidity management are negatively related to firm efficiency. In a nutshell, it can be summarised that, among the small and large size firms, size significantly influences the overall firm efficiency, followed by OPEX and CUR in the case of small-size firms, whereas, in the case of large-size firms, OPEX is highly influenced by the firm efficiency (See Table 15). 6.3. Some caveats to the DEA approach used in this study While we track the efficiencies of the Housing Finance sector using Data Envelopment Analysis, it is critical to understand the overall economic conditions prevalent at the time, which would help us, understand the situation better. These macroeconomic events need a special mention in our study, as the nature of these occurrences is rare, non-cyclical, and all-impacting, combined with the Table 15. Tobit regression model (large size firm) Eff_score Coef. Std. Err. t P>|t| [95% Conf. Interval] FS 1.34E–07 4.52E–08 2.96 0.003 4.48E–08 2.23E–07 CRISK 1.361988 0.7808695 −1.74 0.083 −2.903437 0.1794617 OPEX 0.6076383 0.274476 −2.21 0.028 −1.149458 −0.0658181 CAP 5.02E–06 3.69E–06 −1.36 0.175 −0.0000123 2.26E–06 NIM 8.411848 8.176045 1.03 0.305 −7.727801 24.5515 ROA 9.106399 7.24569 −1.26 0.211 −23.40951 5.196714 POC 0.2543671 8.133479 −0.03 0.975 −16.30999 15.80126 CUR 0.0013506 0.0213828 −0.06 0.95 −0.0435606 0.0408594 LIR 0.2505422 0.2059037 −1.22 0.225 −0.6569995 0.1559151 CRAR 0.0003517 0.0002998 −1.17 0.242 −0.0009435 0.0002401 Source: Author’s calculation. Note: LR chi 2 (10) = 29.13; Prob > chi 2 = 0.0012; Pseudo R 2 = 0.0926. Table 14. Descriptive statistics of variables (large size firm) Variable Mean Std. Dev Min Max Eff_score 0.4476667 0.4122503 0.012 1 FS 426886.5 909590.2 2960.1 5828840 CRISK 0.9190098 0.0712722 0.512312 0.9899569 OPEX 0.7580834 0.147791 0.544966 2.204798 CAP 3127.169 14543.88 110.8 192837.2 NIM 0.0358677 0.0162942 −0.02454 0.1046732 ROA 0.1108074 0.0188274 0.048108 0.1737998 POC 0.0662256 0.0093442 0.036546 0.0872801 CUR 1.159965 2.659705 0.004288 25.77297 LIR 0.1063154 0.1955882 1.63E–05 1.251884 CRAR 29.394 121.7912 −13.91 1651.14 Note: Author’s calculation. Gopalkrishnan et al., Cogent Economics & Finance (2023), 11: 2285158 https://doi.org/10.1080/23322039.2023.2285158 Page 20 of 27 global domino-effect impact they generated. It thus becomes imperative to understand their presence so that the implications of our study can be better grasped, comprehended and implemented. Some notable macroeconomic events that coincided with the period of our study (2013– 2021) include: (i) An increase in the severity of the Non-Performing Assets (NPAs) crisis in India directly affects the efficacy and lending capacities of Indian banks, thereby negatively impacting their credit positions in all other lending sectors. (ii) The aftermath of the Sub—prime and Eurozone-sovereign crisis. (iii) An increase in global geopolitical uncertainty. (iv) The onset of the COVID–19 pandemic and subsequent recovery following the immunisation campaigns. Through several channels, these events affect the sets of input and output variables of financial institutions considered by this study. The paragraphs below add more insights and detail to the specific situations caused in the economy due to these macroeconomic events that coincided with our study period. 6.3.1. NPA crisis in Indian banks At the beginning of this period (2013), India was still experiencing relatively high economic growth, which led to increased lending by banks to various sectors (Mohanty et al., 2022). However, from 2013 to 2021, the Indian banking sector experienced a significant rise in non-performing assets (NPAs), also known as bad loans, which profoundly impacted the overall health of the sector and the Indian economy. The NPA situation during this period was a cause for concern for several banks and posed several challenges to the stability and efficiency of the entire banking system and cast aspersions on the Indian Economy as a whole. However, as economic growth slowed and various industries faced headwinds, borrowers struggled to repay their loans, resulting in a deterioration of asset quality in banks’ loan portfolios (Mohanty et al., 2022; Silva, 2021). The NPA problem was more pronounced, particularly in public sector banks (PSBs) that accounted for a significant share of the Indian banking system. PSBs faced challenges in managing NPAs due to political interference, outdated lending practices, and inefficiencies (Patra et al., 2023). Large infrastructure projects, especially in sectors like power, steel, and construction, faced delays and cost overruns, leading to increased NPAs for banks. The stressed assets in the corporate sector further aggravated the NPA issue (Bhagwati et al., 2017; Gaur & Mohapatra, 2021; Kandi et al., 2022). In 2015, the Reserve Bank of India (RBI) conducted an Asset Quality Review, identifying significant previously unrecognised bad loans, further impacting the banking sector’s health. As the NPA problem escalated, the Indian government implemented recapitalisation measures to infuse capital into PSBs and strengthen their balance sheets. In 2016, the Indian government introduced the Insolvency and Bankruptcy Code to provide a time-bound resolution framework for distressed companies, expediting the resolution process and helping banks recover their dues faster. Banks faced challenges in recovering the NPAs, especially from more significant defaulters, due to legal and procedural bottlenecks. The RBI introduced various measures to address the NPA problem, including stricter norms for loan classification, stressed asset resolution frameworks, and revised provisioning norms. The NPA situation constrained banks’ lending ability, as they became more risk-averse than earlier, and led to a slowdown in credit growth, affecting investment and economic expansion (Dar et al., 2021). Overall, the NPA situation in the Indian banking sector from 2013 to 2021 was a significant concern that required concerted efforts from not only the government and regulatory bodies; but also from Banks themselves to arrive at a time-bound and effective resolution (Mohapatra et al., 2023; Satya Krishna Sharma et al., 2022). While various steps were taken to tackle the issue, it Gopalkrishnan et al., Cogent Economics & Finance (2023), 11: 2285158 https://doi.org/10.1080/23322039.2023.2285158 Page 21 of 27 remained a complex and persistent challenge for the banking sector, impacting credit availability, investor confidence, and economic growth. 6.3.2. Spillovers from international monetary policy In response to the subprime and COVID–19 crises, several central banks of developed nations began engaging in a series of quantitative easing. As a result, these policies have significant spillovers to emerging markets such as India (Cortes et al., 2022). They distort equilibrium capital flows and exchange rates (Dedola et al., 2021). They also generate bubbles in emerging real estate markets. In this context, Jarrow and Silva (2015)observed that traditional risk management practices are ineffective in detecting asset-price bubbles. These forces affect every balance sheet and income statement variable of Indian financial institutions. 6.3.3. Domestic monetary policy It is important to note that the sample period also comprises the important Indian demonetisation episode in 2016 (Chodorow-Reich et al., 2020). This experiment affects every balance sheet and income statement variable of Indian financial institutions. 6.3.4. Fiscal policy and implicit government guarantees In the aftermath of the global financial crisis and response to the COVID–19 pandemic, governments of several countries (including India) enacted fiscal stimulus measures (Benmelech & Tzur- Ilan, 2020). These policies widen government deficits, affecting financial institutions’ credit risk (Silva, 2021). Financial institutions’ interest income, interest expense, and loan origination are affected when credit risk changes. Higher government deficits also affect the strength of government guarantees, which in turn affect the income of financial institutions (Dantas et al., 2023), as well as the value of their equity (Gandhi et al., 2020), and other securities (Kelly et al., 2016). 6.3.5. Geopolitical uncertainty The sample period is also characterised by elevated geopolitical uncertainty. In particular, the Brexit referendum is shown to have essential spillovers to firms’ investments in markets outside the UK (Campello et al., 2022). Given the strong ties between the UK and Indian economies, it is crucial to understand that the uncertainty caused by Brexit may have affected the value of real estate assets in India (Campello et al., 2022; Doyle et al., 2016), which in turn may affect the balance sheets of housing financing companies. 7. Conclusion The efficiency evaluation of the large and small-size HFCs has been analysed with the help of DEA and the Malmquist index. It has been observed that, in the case of large-size firms, only 4 out of 18 firms lie in the efficient frontier, whereas the scenario changed in the small-size firms, with six out of eight firms qualifying to touch the efficient frontier. It has been summarised that most of the HFCs are inefficient for large-sized firms in India. In addition, with the help of the Malmquist Index, it has been observed that, in the case of the productivity change, out of 18, only two firms obtained a productivity score of 1, while the remaining 16 are underperforming. Overall, the total factor productivity change was highest in the year 2020–21 and comparatively decreased in the subsequent year due to the sector crisis witnessed in the Indian economy. In large firms, the size predominately influences efficiency and is highly significant, which was insignificant in small firms. Hence, it signals to the market that small firms must maximise their efficiency by maintaining their size. Regarding risk efficiency, both firms are negatively impacting the firm efficiency, which indicates the operating expenses are to be taken care of to achieve the efficiency level. It also reveals that, in the case of liquidity management, large firms are insignificant to affect the firm efficiency, whereas in the case of small size firms, though it is negatively influencing, it is highly significant. It concludes that small firms are to be cautious in terms of liquidity management as compared to large firms. As the firm performance signifies a predominant role in the evaluation of the firm efficiency, in the current study, both the firms Gopalkrishnan et al., Cogent Economics & Finance (2023), 11: 2285158 https://doi.org/10.1080/23322039.2023.2285158 Page 22 of 27 are incapable of achieving the same, as the coefficients are insignificant, hence large as well as small firms must be prudent enough to optimise their performance in the future to retain their market value in the economy. Therefore, it can be summarised that large firms have to internally restructure their coefficients to achieve efficiency, whereas small firms are in their given capacity to maintain efficiency in the provided scenarios. 7.1. Limitations and future scope The study grapples with certain limitations inherent to the Indian Housing Finance Company (HFC) sector and the broader non-banking financial industry. These limitations are primarily attributed to the sector’s unique characteristics and the evolving regulatory landscape, which can result in a scarcity of comprehensive and granular data. This data scarcity extends to variables that could potentially serve as instrumental variables or effective controls to address endogeneity, further complicating the study’s ability to mitigate this issue. Additionally, due to the nascent stage of these industries, access to historical data series of the same depth and breadth as more mature financial sectors is limited. In many cases, available data primarily centres on financial performance, regulatory compliance, and broad economic indicators, leaving researchers with limited options for constructing instrumental variables or effectively controlling for endogeneity. In the context of this evolving and data-constrained landscape, compounded by the relatively small number of companies in the NBFC and HFC sectors, the study inherently faces constraints in comprehensively addressing endogeneity issues. Recognizing and acknowledging these data limitations is pivotal for maintaining transparency and understanding the distinct challenges associated with empirical research in the Indian NBFC and HFC sectors. Therefore, these constraints underscore the need for ongoing data collection and analysis to facilitate more profound investigations into these industries. Moreover, it’s imperative to note that this study is limited to housing finance companies based on the asset size criteria established by the Reserve Bank of India in consultation with the National Housing Bank. The potential for future research in this domain is promising, contingent upon data availability. Subsequent studies could consider extending the analysis to cover additional periods and further diversifying the HFCs by categorizing them into clusters based on ownership (public, private, government-owned) and operational characteristics (deposit-taking and non-deposit-taking). Furthermore, future studies in this area could adopt more advanced methodologies, such as a refined Data Envelopment Analysis (DEA), to enable a more intricate analysis. This, in turn, could provide a more nuanced understanding of the functioning of the often-debated and complex sector in the economy, contributing to the enhancement of the country’s financial system. Acknowledgments The authors are grateful to the journal’s anonymous referees for their extremely useful comments to improve the article. Funding The author has no financial support for this research work or article publication. Author details Santosh Gopalkrishnan 1 E-mail: [email protected] ORCID ID: http://orcid.org/0000-0001-7310-6330 Shiba Prasad Mohanty 1 ORCID ID: http://orcid.org/0000-0001-6941-6221 Megha Jaiwani 1 ORCID ID: http://orcid.org/0000-0002-7588-6702 1 Symbiosis Institute of Business Management, Pune, Symbiosis International (Deemed University), Pune, India. Disclosure statement No potential conflict of interest was reported by the author(s). Supplemental material Supplemental data for this article can be accessed online at https://doi.org/10.1080/23322039.2023.2285158 Citation information Cite this article as: Do efficiencies really matter? Analysing the housing finance sector and deriving insights through data envelopment analysis, Santosh Gopalkrishnan, Shiba Prasad Mohanty & Megha Jaiwani, Cogent Economics & Finance (2023), 11: 2285158. Note 1. As per the report released by the RBI in 17th June 2020,All HFCs, regardless of asset size or ownership, must be treated equally. In other words, nondeposit taking HFCs with assets of 500 crore or more, as well as all deposit taking HFCs, regardless of asset size, will be considered systemically important HFCs. HFCs with assets less than 500 crore will be classified as non-systemically important in India. 2. The transition of HFC supervision from the NHB to the RBI was announced for FY2020; with this change, the RBI now supervises and governs all players in the housing finance sector. Gopalkrishnan et al., Cogent Economics & Finance (2023), 11: 2285158 https://doi.org/10.1080/23322039.2023.2285158 Page 23 of 27