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The Indian real estate sector

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Published: Mon, 5 Dec 2016

The Indian real estate sector plays a significant role in the country’s economy. The real estate sector is second only to agriculture in terms of employment generation and contributes heavily towards the gross domestic product (GDP). Almost 5 per cent of the country’s GDP is contributed to by the housing sector. In the next five years, this contribution to the GDP is expected to rise to 6 per cent.

According to Jones Lang LaSalle, faster economic growth in Brazil, Russia, India and China (BRIC) could result in the property markets of those nations recovering at a faster rate than the UK and US real estate markets. It has also been suggested that India’s property sector could begin to improve from late 2009 and may attract up to US$ 12.11 billion in real estate investment over a five-year period.

Almost 80 per cent of real estate developed in India is residential space. According to the Tenth Five Year Plan, there is a shortage of 22.4 million dwelling units. Thus, over the next 10 to 15 years, 80 to 90 million housing dwelling units will have to be constructed with a majority of them catering to middle- and lower-income groups. It is for this reason that residential properties in India, particularly in Mumbai and Delhi, are viewed as very good investments as per a study by PricewaterhouseCoopers (PwC) and Urban Land Institute, a global non-profit education and research institute.

In the 2009-10 budget, developers of affordable housing projects (units of 1,000-1,500 sq ft) have been granted a tax holiday on profits from projects initiated in the financial year 2007-08. Such projects would have to be completed before March 1, 2012. At the same time, the finance minister allocated US$ 207 million to grant a 1 per cent interest subsidy on home loans up to US$ 20,691, provided the cost of the home is not more than US$ 41,382. This subsidy is expected to give a further boost to the housing sector.

An apartment is a self-contained residential unit or section that occupies a part of a building. It can be either owned or rented. Some apartment-dwellers own their apartments as cooperatives, in which the dwellers or residents own shares of a corporation that owns the building or development. In condominiums, residents own their apartments and share ownership of the public spaces.

Living in apartments is gaining popularity in India. The Sahara Group has already decided to build 217 townships across India. Their allure lies in the convenience that they offer in terms of safety and security and maintenance of utilities like electricity and water. A central maintenance system obviates the need for hiring outside help for minor problems like leaking taps or electric short circuits. Stand-alone homes also require incurring additional costs like buying/leasing land, licensing, duties, etc. Apartments enable maximization of space utilization and reduce demand on public resources. People are also able to avail of additional amenities like gymnasiums, swimming pools, etc. at affordable prices.

There is a gap in the literature, however, with regard to the value drivers that dictate purchase decisions of residential property in the country. Similar studies exist for other countries but were found wanting in the Indian context, especially when it comes to apartments. Through this paper, we aim to do the very same, i.e. establish which factors dictate purchase decision and to what extent. We will also correlate these preferences with the demographic profiles and characteristics of our respondents and hence arrive at a greater and much deeper understanding of these issues. We see immense utility for our paper, especially for builders and property dealers who can use our findings in structuring their own business activities.

RESEARCH BACKGROUND AND HYPOTHESIS:

Even though consumer behaviour is generally assumed to be an important part of real estate valuation, buyer preferences are generally not considered during the valuation process. It is basically reduced to the confirmation of a bid price which may or may not be met by the buyer. Efforts are being made to address this fault and many papers have been written on the analysis of motivations of residential property purchasers, attempting to explain them using models such as bounded rationality and hedonic pricing. Hedonic Pricing, or Hedonic Demand Theory as it is also known, decomposes the item of interest into constituents and evaluates the importance of each of them and their contribution to the overall valuation. These factors can be both internal characteristics of the good or service and external factors. In the case of real estate valuation, internal characteristics include layout, structure, etc of the property while status of neighbourhood, proximity to schools, etc are the external factors. Factor Analysis enables us to do just that. It is a statistical method that reduces the number of variables by grouping two or more of them into unknown or hidden variables known as factors. Further analysis is then conducted by looking at the variation among these factors and evaluating their relative performance. These factors are taken to be linear combinations of the original variables plus error terms (Richard L. Gorsuch, 1983).

“Factor analysis seeks to do precisely what humans have been engaged in doing throughout history – that is to make order of the apparent chaos of the environment” (Child, 1990). It has great use in evaluating consumer behaviour. Charles Spearman is credited with its invention. He used it in the formulation of the ‘g Theory’ as part of his research on human intelligence (Williams, Zimmerman, Zumbo & Ross, 2003). Over the years it has found uses in fields as diverse as psychometrics, marketing, physical sciences and economics. It can be used to segment consumers on the basis of what benefits they want from the product/service (Minhas & Jacobs, 1996). It has evolved as a technique over the years, with many researchers working on fine-tuning and improving the analytical process. Bai & Ng (2002) developed an econometric theory for factor models of large dimensions. It focused on the determination of the number of factors that should be included in the model. The basic premise of the authors was that a large number of variables can be modeled by a small number of reference variables.

Marketing strategies based on customer preferences and behaviour often make use of this technique during the market research phase (Ali, Kapoor & Moorthy, 2010)[14] and while devising and changing the marketing mix (Ivy, 2008). Factor Analysis has also been used in ground water management to relate the spatial distribution of different chemical parameters to various sources (Love, Hallbauer, Amos & Hranova, 2004).

The facility of segmentation that factor analysis offers has been extended to the real estate sector and all studies thereof. Regression analyses are subject to aggregation biases and segmented market models yield better results. This segmentation is done using factor analysis Watkins, 1999). Property researchers have also dedicated a lot of attention to researching the preferences of property buyers and identifying the drivers of property value. A study in Melbourne, Australia (Reid & Mills, 2004) analyzed the purchase decisions of first time buyers and tried to determine the most influential attributes that affect the purchase decision using factor analysis. The research findings of the paper indicated that financial issues accounted for approximately 30 percent of the variance in the decision of first time owners to buy housing. This related to timing, the choice of housing, and the decision to buy new housing. Apart from that the choice of housing is dependant on Site Specific factors (Location) and the decision to buy new housing is dependent on Lifecycle factors, such as family formation, marital status or the size of the existing house. Another study determined that brand, beauty and utility play a defining role in property value (Roulac, 2007). The findings of the paper explain why certain properties command premium prices, relative to other properties. It came to the conclusion that for value determination of high priced properties the overall perception of the brand is the most important factor followed by utility and beauty. Brand names are also very important especially in metropolitan markets as they add to the appeal, distinctiveness of the property. Another way to attract buyer’s attention is through the mix of neighborhood amenities offered (Benefield, 2009). Neighborhood amenities like tennis courts, clubhouses, golf courses, swimming pool, play park and boating facilities significantly impact property values. Hedonic pricing models in which buyers are assumed to evaluate property specifics and location attributes separately when they purchase a home have also been used to study housing markets like that of Shenzhen, China (Xu, 2008). The findings suggest that the marginal prices of key housing attributes are not constant. Instead, they vary with the household profile and location.

Cluster analysis involves the grouping of similar objects into distinct, mutually exclusive subsets known as clusters. The objective is to group either the data units or the variables into clusters such that the elements within a cluster have a high degree of natural association among themselves while the clusters remain relatively distinct from one another. Mulvey and Crowder (1979)[22] presented and tested an effective optimization algorithm for clustering homogenous data. Punj and Stewart (1983)[23] reviewed the applications of cluster analysis to marketing problems. They presented alternative methods of cluster analysis to evaluate their performance characteristics. They also discussed the issues and problems related to use and validation of cluster analysis methods. The application of cluster analysis in strategic management research was studied by Ketchen and Shook (1996). Their paper chronicles the application of cluster analysis in strategic management research. They analyzed 45 published strategy studies and offered suggestions for improving the application of cluster analysis in future inquiries. They believed that cluster analysis is a useful tool but the technique must be applied prudently in order to ensure the validity of the insights it provides.

Since Marketing researchers were introduced to discriminant analysis half a century ago, it has become a widely used analytical tool since they are frequently concerned with the nature and strength of the relationship between group memberships. It is especially useful in profiling characteristics of groups that are the most dominant in terms of discrimination. Morrison (1969) explained how discriminant analysis should be conducted using canned applications and how the effect of independent variables should be determined. However, care must be taken when applying discriminant analysis. The potential for bias in discriminant analysis has long been realized in marketing literature. Frank, Massy and Morrison (1965) showed that sample estimates of predictive power in n-way discriminant analysis are likely to be subject to an upward bias. This bias happens because the discriminant analysis technique tends to fit the sample data in ways that are systematically better than would be expected by chance. Crask and Perreault (1977) looked at the validation problems in small-sample discriminant analysis.

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