Factors affecting rentals of residential apartments in Pune, India: an empirical investigation

Abstract

Purpose The purpose of this paper is to find out the factors affecting rentals of residential apartments in Pune, India. Design/methodology/approach Four regression models are developed, i.e. basic ordinary least square (OLS) regression model, OLS regression model with robust estimates, OLS regression model with clustered robust estimates and generalized least square (GLS) regression model with maximum likelihood (ML) robust estimates. Based on the Akaike information criterion and Bayesian information criterion criteria, OLS regression model with clustered robust estimates and GLS regression model with robust estimates are best fit. The data are tested for multicollinearity and the models are tested for heteroscedasticity. The study uses the expected rent value data collected from Web portals and the data on factors affecting the rental value of residential property are collected through the study of land use maps, Google earth software and field visits. Findings Total floor area and number of rooms are structure related factors that positively affect the rental value, i.e. more the area and number of rooms, higher the rental value. The distances from the nearest police station and fire station are security and safety factors. The results suggest that higher distance from these factors leads to lower rental values, as safety and security is the top priority of residents seeking residential property on rental basis. The distance from employment zones, distance from nearest school/college and the distance from the nearest public transport terminal are convenience related factors that negatively affect the rental value, as greater the distance, lesser the rental value and vice versa. The distance from Central Business District and hospitals has a positive effect on the rental values of a residential property implying that higher distances from these places command higher rental value. Research limitations/implications The study relies on rental data that owner is expecting for a particular property, it is not certain that the property would be actually rented for the same value. Second, researchers had to drop certain important drivers of rental value because of the issue of multicollinearity. Practical implications This is one of the rare studies conducted in Indian context, and the findings of the study are useful from the owner, tenants, urban bodies and developers’ point of view. Knowing that India is one of the fastest growing markets and need for housing is increasing day by day (including housing facility on rental basis), the stakeholders need to take care of the factors that affect the rental values of a residential property. Social implications The authors suggest the governments and the municipal bodies in India to come up with a public rental housing policy that separately caters to the needs of the lower income group, middle and upper income group in at least metros, tier I and tier II cities that are witnessing unprecedented growth in job seeking immigrants, who are seeking properties on rental basis. While developing a public rental policy, they must keep in mind the factors that are driving the rental values, such as proximity to employment zones, proximity to proper school and college, efficient public transport system as well as all safety and security measures. Creation of such a public rental policy is a win–win situation for immigrants, property owners and government/urban development bodies. Originality/value This paper is the first empirical study about the factors affecting rental values in Pune, India. The study will help property owners, immigrant and local tenants, government and urban development bodies to develop an understanding about the important factors affecting rental value and come up with their respective plans. Advanced econometric regression models are used based on the data that is collected through actual field visits, study of maps and secondary information rather than use of survey method or creation of dummy variables.

Identifying the effect of retail brands on private residential rental prices in Great Britain

“…Homing in on the impact of one dimension of housing price requires controlling for a number of other covariates, each of which are generally either structural or locational in nature (Can, 1992 ; Singla & Bendigiri, 2019 ). Structurally, consensus has been reached on the main factors influencing price; notably that the age, type and size of the property are important.…”

Comparing predictive performance of general regression neural network (GRNN) and hedonic regression model for factors affecting housing prices in “Pune-India”

“…Hedonic models are most commonly estimated using regression analysis and are applied in different sectors (Babawale et al , 2012). Such hedonic regressions can be used in simple linear form, log linear form and with double log function as desired and suited to the study (Singla and Bendigiri, 2019). For instance, Keskin (2008) explored the factors affecting housing prices in Istanbul, Turkey using a hedonic logarithmic function and Kostov (2009) used a semi parametric regression-based estimation.…”

“…However, despite all these efforts and reforms, the demand for home buyers has not gone up in this period. According to a report published in Times of India (2019) (December 26th 2019), “there are about 183,000 unsold housing units (total unsold built-up area across major cities was close to 225 million square feet) in urban areas worth INR13,93,750.00m.” As also pointed by Singla and Bendigiri (2019) that because more and more job opportunities are coming in metro, Tiers I and II cities, more and more people from other states are migrating to such cities, but they are mostly seeking apartments on rental basis. What can be the possible reasons for this behavior is something that scholars are trying to explore ( Ma et al ., 2019 ).…”

Identifikasi Faktor-Faktor Yang Mempengaruhi Nilai Properti Residensial Berdasarkan Persepsi Penilai Pemerintah

“…Meskipun aksesibilitas menentukan waktu dan biaya perjalanan, faktor kenyamanan lingkungan secara empiris terbukti mempengaruhi permintaan akan suatu properti. Lingkungan yang bersahabat dan memiliki fasilitas yang lengkap (Chesire dan Sheppard, 1995), unsur keamanan yang diperoleh dari rumah berbasis culuster (Rahadi, Wiryono, Koesrindartoto, & Syamwil, 2012, p. 868) atau dekat dengan kantor polisi (Singla Harish & Bendigiri, 2019), kedekatan dengan ruang terbuka (Irwin, 2002), kualitas udara yang bagus (McCord Michael, MacIntyre, Bidanset, Lo, & Davis, 2018), fasilitas lapangan golf (Do & Grudnitski, 1995), ketersediaan ruang hijau (Conway, Li, Wolch, Kahle, & Jerrett, 2010) atau perumahan bernuansa hijau (Rahadi Raden et al, 2015), pemandangan alam atau perairan (Benson, Hansen, Schwartz, & Smersh, 1998) Penelitian lain mengenai properti residensial menemukan bahwa penduduk yang berpenghasilan lebih tinggi rela membayar lebih mahal untuk tinggal di lingkungan yang lebih homogen (misalnya latar belakang ekonomi atau Pendidikan tertentu) (Goodman & Thibodeau, 2003). Jenis perumahan eksklusif yang memiliki ruang rekreasi keluarga pribadi, taman, dan fasilitas gimnasium memiliki daya beli, jauh lebih diminati dan tentunya jauh lebih mahal dibanding perumahan eksklusif lainnya di Hong Kong (Raymond dan Love, 2000).…”