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Property management in commercial real estate (CRE) is an important operational function that needs to be managed because it brings large cost implications to the organization. As India aspires to become a developed real estate market, analysis of the growing importance of automating property services and technology acceptance by stakeholders are two key concerns that need to be explicitly addressed. This study aims to examine the extent of property technology (PropTech) adoption in India and propose a technology-enabled stakeholder management model in Indian CRE.

The research is qualitative in nature and follows the grounded theory approach. Research data were collected by conducting a series of semi-structured interviews with 18 property management professionals from different prominent Indian companies using PropTech.

The findings suggested the nine most typical automated property management functions in Indian CRE. The result of this research is the automated property services model for stakeholder management in CRE. The model demonstrates the value of implementing technology in property services in India.

The study provides useful insights into how artificial intelligence (AI) in property management can be applied to address property-related challenges, various stakeholder needs and improve property performance in accordance with energy efficiency policies.

This paper attempts to add to the limited body of literature on technology in the property management domain. The model demonstrates how automated property services meet the needs of different stakeholders in CRE and provides remote working procedures within the COVID-19 pandemic context.

Establishing the strength of a novel variable–mortgage debt as a fraction of US gross domestic product (GDP)–on forecasting capitalisation rates in both the US office and multifamily sectors.

The authors specify a vector error correction model (VECM) to the data. VECM are used to address the nonstationarity issues of financial variables while maintaining the information embedded in the levels of the data, as opposed to their differences. The cap rate series used are from Green Street Advisors and represent transaction cap rates which avoids the problem of artificial smoothness found in appraisal-based cap rates.

Using a VECM specified with the novel variable, unemployment and past cap rates contains enough information to produce more robust forecasts than the traditional variables (return expectations and risk premiums). The method is robust both in and out of sample.

This has direct implications for governmental policy, offering a path to real estate price stability and growth through mortgage access–functions largely influenced by the Fed and the quasi-federal agencies Fannie Mae and Freddie Mac. It also offers a timely alternative to interest rate-based forecasting models, which are likely to be less useful as interest rates are to be held low for the foreseeable future.

This study offers a new and highly explanatory variable to the literature while being among the only to model either (1) transactional cap rates (versus appraisal) (2) out-of-sample data (versus in-sample) (3) without the use of the traditional variables thought to be integral to cap rate modelling (return expectations and risk premiums).