Purchase decision of apartments in metropolitan India

Purchase ending of flatbeds in metropolitan India chemical elements affecting the obtain decision of apartments in metropolitan IndiaAbstractPurpose The purpose of this paper is to provide an cortical potential into the motivation behind Indian buyers when looking to barter for an apartment. The factors driving demand cullences for apartments ar not well established and are difficult to measure, and often builders may not generate an insight into what buyers are looking for.Design/methodology/approach The inquiry in this paper is based on telephonic interviews and internet based survey with recent procurers, who bought a home in the past 1 grade and prospective purchasers looking to buy an apartment in the coming one year. They belonged to number of reparations across both metropolitan cities of India Delhi, Mumbai, Bangalore, Kolkata and Chennai. The data were analysed using factor synopsis to identify the criteria in an apartment that buyers value the to the high -pitchedest degree. This research was done across all ages and irrespective of their intention of why they bought or if this was their first purchase. Further, Cluster analyses was use to determine bunch ups and one way Anova was utilize to determine the factors that h one-time(a) different value to different clusters of people. Discriminant Analysis was used to determine any loss in behaviour of first time purchasers with former(a)s.Findings The findings in this paper revealed that issues signifying affluence accounted for approximately 27 percent of the choice of housing by Indian buyers to purchase apartments in metropolitan India. Also, Cluster Analysis revealed that demographically different forwardness of buyers differ signifi disregardtly in their attitude towards Financial factors. Discriminant compend revealed that first time buyers hold signifi tintly more importance to Financial factors same(p) House price, Income where they give more than lesser importance to B uilder reputation and Status of region.Research limitations/implications The research in this paper is aimed specifically at Indians living in metropolitan cities only which may be very different from the pillow of India. The majority of the respondents belong to Delhi, which may in like manner bias the results. The majority of the data has been collected from an online survey which may reduce the severity of the findings.Practical implications If due precondition is given to the factors that buyers are most concerned about, builders of new apartment housing would be better equipped to meet this demand and maximise their profits. Builders will also be able to target buyers better by knowing the difference in preference of first time buyers to others.Originality/value This paper provides an invaluable insight into Indians concept of a suitable apartment in metropolitans. While important decision factors were dictated for the entire population, further analysis was done to de termine difference in issues felt important to first time buyers. Also, the most important factors were determined for different demographic clusters. Thus in this way, the transaction of purchasing an apartment was analyzed from several points of view. Keywords Consumer behaviour, Purchase, Apartment, IndiaPaper type Research paperINTRODUCTIONThe Real Estate sector is important to the Indian economy. In terms of meshing generation, it is second only to the agricultural sector. The housing sector contributes nearly 5% to Indias GDP. It is expected to rise to 6 per cent in the next quin years. piazza markets in India are recovering faster than those in the US and the UK. The sector is expected to attract around US$ 12.11 billion of investments in the next five years. Residential space comprises almost 80% of the real estate developed in the country. There is a shortage of 22.4 million dwelling units according to the 10th Five Year Plan. 80 to 90 million housing units will ware to be constructed over the next 10 to 15 years to furbish up this, with the majority of them for the middle- and lower-income groups. It is for this reason that residential puritanicalties in India, particularly in Mumbai and Delhi, are viewed as very rock-steady 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, a tax holiday on profits was granted to developers of affordable housing (units of 1,000-1,500 sq ft). This privilege was instituted for projects that started from 2007-08 onwards with a deadline of completion of March 1, 2012. US$ 207 million was also allocated to grant a 1% interest subsidy on home loans up to US$ 20,691 with the caveat that the cost of the home should not be more than US$ 41,382. This was expected to further help the housing sector.An apartment is a residential unit that forms a division of a building. It can be either owned or rented. Some pe ople own their apartments together where each owns a part of the corporation which owns the flat. In condominiums, dwellers own the individual apartments and share the public environment.Living in apartments is gaining popularity in India. 217 townships across India are in the building plans for the Sahara Group. Their captivate 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 fate 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, and so on Apartments enable maximization of space utilization and reduce demand on public resources. People are also able to usefulness of additional amenities like gymnasiums, swimming pools, etc. at affordable prices. There is a gap in the literature, however, with regard to the value driver s 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 on a lower floorstanding 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 charge 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. Ef forts are being made to carry on 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 tenableness 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 eon 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 mathematical group two or more of them into unknown or hidden variables known as factors. Further analysis is then conducted by looking at the wavering a mong 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 field 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 proficiency over the years, with many researchers running(a) on fine-tuning and improving the analytical process. Bai Ng (2002) developed an ec onometric 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) and while devising and changing the marketing mix (Ivy, 2008). Factor Analysis has also been used in ground water management to relate spacial distribution of unhomogeneous chemical parameters to different 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 devote a lot of economic aid 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 explain about 30% of the variance in the purchase decisions of first time house-owners. This related to timing, the choice of housing, and the decision to buy new housing. Apart from that the choice of housing is dependent on Site Specific factors ( localization principle) 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 expl ain why certain properties command exchange 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. mark off names are also very important especially in metropolitan markets as they add to the appeal, distinctiveness of the property. Another way to attract buyers 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 affect property values. Xu (2008) used a hedonic pricing model to study the housing market of Shenzhen, China. He operated under the assumption that buyers consider property specifics and location attributes separately when they buy a home. The findings suggest that the marginal prices of attributes are not constant. Instead, they vary with the hou sehold profile and location. Cluster analysis involves the grouping of similar objects into distinct, reciprocally 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) pre displaceed and scrutinyed an effective optimization algorithm for clustering homogenous data. Punj and Stewart (1983) reviewed the drills 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. Ketchen and Shook (1996) chronicled the application of cluster analysis in strategic management research. They analyzed 45 published strategy studies and offered suggestions for improvi ng 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 constitution 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 agnise 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. motley research papers have studied the features that are evaluated while purchasing a home, how these features factor in terms of pricing the residences and how the home owners rate the various scales on importance. Such studies, however, were found lacking in the Indian context. This paper aims to understand the value drivers of apartments in Indian metros using factor analysis. The initial variables that we have considered are as follows House Price This refers to the price/rent that is being charged for the apartment. The real estate market is often segmented using this variable. handiness of Gymnasium, Swimming Pool and other sports facilities Many apartment complexes and housing societies offer recreational facilities to the residents to service their lifestyle needs. Traffic This variable refers to the density of vehicular gesture in the location in which the apartment is located. size of Individual Rooms The size of the rooms within the apartment is also an important factor. Some buyers prefer big, airy rooms while others might want smaller rooms. Proximity to City This refers to the location of the apartment relative to the city boundaries, i.e. whether it is within the city proper or on the outskirts. Ability to obtain Loans This variable stands for the ease with which the buyers can get loans, either through the builder or on their own. Parking distance The availability of parking space is considered important by some consumers. Exterior Look of the Apartment This refers to the faade of the apartment, i.e. whether its attractiveness is a strong enough motivation. family unit Income The total income of t he household often dictates the purchase decision of families. Perceived Safety of Locality This is a big concern for some customers, especially single women and old people and may significantly influence the purchase decision. Branded Building brokers Some consumers may value an apartment more if it has branded fittings, furnishings, etc. cipher from the apartment This can be an important variable for some customers. Preference for Ground Floor This variable refers to the customers preference for the ground floor relative to other floors. Water Supply This variable way to measure how important it is for the consumers that there is continuous, guaranteed and good quality water supply. Structure This refers to the layout of the apartment whether it is a 2BHK or 3BHK, etc. Status of Neighbourhood For some consumers, the reputation and social standing of the locality that they live is very important. Proximity to Shops and Parks This seeks to measure whether proximity to th ese places is an important quantity for buyers or not. Interior Design This refers to interior features of the apartment like flooring, lighting, balcony, etc. Availability of Domestic friend This can be important consideration, especially for working couples. Proximity to Schools and Offices This seeks to ask how important such proximity is to the buyer. Builder Reputation Many buyers are heavily influenced by the brand name and reputation of the builder. monthly Living Costs Certain average monthly expenditure is incurred as living expenses. We seek to gauge the relative value of this variable. Proximity to Public Transport, study Roads, etc This refers to the accessibility of the apartment with regard to public transport and roads. Power Backup Full power backup in case of power outages is frequently denote by builders. Whether this actually influences buying behavior needs to be examined. Proximity to friends/relatives homes This can be a big variable that dictates consumers in their decision-making process.MethodsSampleThe questionnaire was sent to people residing in Indian metropolitan cities. Out of the 172 responses received, 13 were rejected since the respondents had not purchased a property in a metropolitan city. Another 13 were rejected because either the respondents had not purchased the apartment in the last one year or were undecided as to when to purchase the property. Finally out of all the respondents 146 (84.9%) were identified. MeasuresThe 25 variables were measured by a Likert scale with responses ranging from 1 (Very Low Importance) to 5 (Very High Importance). AnalysisThis study uses four tests to analyze the factors involved in purchase of an apartment. The first test conducted is the factor analysis which is used to club the variables in order to determine the purchase criteria of apartments. Thus, in this analysis the broad set of variables will be constricted to determine the smaller set of factors that can explain what home owners look for when purchasing an apartment. After this, a cluster analysis was conducted to determine the various clusters (groups) that exist within the demographic population. On the above said factor analysis and cluster analysis, a one way analysis of variance was conducted in order to determine the order of preferences of each factors amongst such clusters. Finally, a discriminant analysis was conducted to identify factors that best differentiate the first time purchasers with others. ResultsThe first test conducted was the factor analysis. Under this test, we followed the Principal Component Analysis method on the 25 variables to combine the correlated variables into factors. The KMO value calculated is 0.799 is above the suggested value of 0.5 which indicates that it is good idea to proceed with Factor Analysis. On the basis of the computations as represented in the Rotated Component Matrix (Table 1), the following factors were received Affluence, Financial, location, lifestyle, Site-Specific. The variables were classified into a factor if their loading for the respective factor was greater than 0.4. Also, two other unnamed factors were received which remained so due to the fact that no factor can be formed between two variables. We have followed the Kaiser criterion (1960) of retaining only those factors that are greater than one. The initial research on 25 variables was reduced as the variables on domestic help, floor and proximity to friends/relatives was removed after the factor analysis was done. Domestic help was removed because it loaded on three factors (Financial, Location and Lifestyle) equally. Preference of Ground Floor was removed from the analysis as it showed a positive loading and negative loading on each of two factors which means that while some considered ground floor to be in consideration other considered the penthouse to be better. Proximity to friends/relatives was removed as it was the only variable in factor 6 (unnamed) a nd thus no factor can be made by one variable. The results of the Factor Analysis are as underRotated Component MatrixVariable NameAffluenceFinancialLocationLifestyleSite-SpecificUnnamedUnnamedFactor 1Factor 2Factor 3Factor 4Factor 5Factor 6 Factor 7Traffic0.768Gym/Pool/Sports Facility0.755 judgment from Apartment0.721Builder Reputation0.644Parking Space0.568Status0.513Monthly Cost of Living0.764Household Income0.735Availability of Loan0.691Availability of Domestic Help0.4980.4140.435Proximity to Schools/Office0.778Proximity to Transport0.607Proximity to City0.5750.424-0.401Proximity to Shops/Parks0.546Interior Design0.768Branded Components0.712Power Backup0.594Structure0.741Size0.5800.598Safety0.549Preference of Ground Floor-0.4150.423Proximity to Friends/Relatives0.845Water Supply0.4100.652House Price0.4050.508Exterior Look0.4260.405-0.464Extraction Method Principal Component Analysis. Rotation Method Varimax with Kaiser Normalization.Rotation converged in 21 iterations.Table 1Fac tor Loadings- Purchase of an Apartment Table 2Factor AnalysisFactor No.Factor NameEigen ValuesTotal Variance (%)Cumulative Variance (%)1Affluence 6.82627.30627.3062Financial2.911.60038.9063Location1.8357.34246.2484Lifestyle1.5046.01652.2645Site-Specific1.4475.78858.05261.1294.51662.56871.0594.23666.804The second test that was conducted was the Cluster analysis and has done to segment the respondents on demographic variables of Age, Gender, City and Number of members in the family. Squared Euclidean distance and average linkage hierarchical clustering method was used. At fusion coefficient value of 1.0, two distinct clusters were evident. On conducting a One way ANOVA to compare means with the demographic variables we observe that the two clusters are differ on the mean age with a moment of 0%. The first cluster consists of a younger population with an average age of 37 approximately and the s