Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/26859
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dc.contributor.authorGhelani, Mohak Rajendra-
dc.contributor.authorJain, Tina-
dc.date.accessioned2023-10-10T09:21:28Z-
dc.date.available2023-10-10T09:21:28Z-
dc.date.issued2023-09-09-
dc.identifier.urihttp://hdl.handle.net/11718/26859-
dc.description.abstractInsolvency laws framework in India underwent a major revamp post the promulgation of the Insolvency & Bankruptcy Code, 2016. Although significant progress has been made from the erstwhile insolvency law regime, time-bound resolution of insolvency cases is still a major cause of concern. Among all industries, the real estate sector has the highest proportion of ongoing insolvency cases as a percentage of the total number of cases admitted in each of the sectors. The value of distressed assets deteriorates with time, and delays in insolvency proceedings impact the recoverability for creditors. This warrants some proactive measures from the industry stakeholders to discern early warning signs of financial distress in order to take steps to mitigate the event of insolvency in the first place. In this study, we have developed a corporate insolvency prediction model using logistic regression, for the Indian real estate sector based on specific financial and non-financial information. With this we intend to lay down certain parameters that can assist in identifying the signs of imminent financial distress. The companies can then utilise this information to re-evaluate their corporate strategy and take proactive measures to mitigate the likelihood of default. The larger industry stakeholders can also utilise it to evaluate their exposure to real estate companies based on their short-term probability of filing for insolvency.en_US
dc.language.isoenen_US
dc.publisherIndian Institute of Management Ahmedabaden_US
dc.subjectIndian real estate Sectoren_US
dc.subjectCorporate Insolvency predictionen_US
dc.titleCorporate Insolvency prediction for Indian real estate Sectoren_US
dc.typeStudent Projecten_US
Appears in Collections:Student Projects

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