Keywords

Real Estate, Consumer Purchase Intention, Theory of Planned Behaviour, Structural Equation Modelling.

Introduction

On 11^th March, 2020, the World Health Organization (WHO) announced Covid-19 a public health emergency & a pandemic. This outbreak brought severe repercussion to all export-import business, trading activities, sales and transaction business, all types of products got impacted badly resulting painful economic recession across the globe. The GDP of almost all countries fell significantly. Lockdown and restrictions have seriously damaged nation’s economy. From psychology perspective, all the stakeholders have shown worries and anxiety resulting no purchase or no investment in high involvement products. The covid-19 emergence has made every individual to contemplate that the future will not be the same anymore. A change of such unprecedented proportions too has brought a shift in the ecosystem of Indian real estate. A comprehensive focus of consumers & investors on health, hygiene, security & wellbeing, have brought massive transformation in the land scape of India real estate. On 2nd July 2021 in The Times of India, Moody’s investor service stated that they expect India’s GDP to grow at 9.6% in 2021 and 7 percent in 2022. Also, this is due to easing of restrictions by states, economic activity in May is likely to signify the trough. Real estate sector in India will contribute 13 per cent to India’s GDP by 2025.

As per Abhishek (2021), “Affordable housing is undoubtedly one of the prominent contributors in the Indian real estate industry, especially in a country like India. The year 2020 started with a bang for this segment of real estate as it made a speedy recovery in the initial few months”. Adding to this, Finance Minister Nirmala Sitharaman announced some beneficiary measures for affordable and mid-range housing projects. However, things drastically changed as the coronavirus hit the nation and real estate emerged as one of the worst affected sectors due to the covid-19 pandemic. In a report on Indian real estate outlook by stated that the business activities have started gaining momentum and is rebounding in 2021 due to easing of lockdown & travel restriction. The market sentiments are going good with commercial demand in e-commerce, healthcare, FMCG increasing at a decent pace and the increase in investor presence is expected to drive Indian realty sector in 2021.

According to the PAN India Residential Market survey conducted by Anarock in 2020, the Q2 was the worst hit quarter and the new launches were the lowest since 2013 in both the residential and commercial segment. The supply of materials and labor was also not adequate. Covid-19 had a terrible impact on the availability of labor as they moved back to their native places owing to uncertainty in the job market. The situation has worsened as not the entire labor force has returned to work after Covid-19 norms getting relaxed.

Coming to 2021, work from home has picked demand for affordable house having price band below 50 lakhs is in demand in metros and a ticket size below Rs. 40 lakhs is rising demand in Tier2 and Tier 3 cities/towns. The income tax benefit for affordable housing, which has been increased by an additional year, for buyers as well as builders, will further boost the housing demand. Also, the government mission ‘Housing for All’ launched in 2015 to build 2 crore affordable houses by 31^st March 2022 is intended for urban poor has boosted this segment. There has been rate cut in GST (Goods and Service Tax) from 8 percent to 1 percent, this change is boosting demand for affordable housing for consumers and investors. Undoubtedly, the consumer behaviour gets impacted due to such unprecedented events and for such long duration. As a result, the consumer and investor psychological pattern will take time and turn to showcase purchase behaviour. Although, this events also gives opportunity to buyers and investors to purchase property at minimal price. For many people, work from home has been a new reality and people slowly have started understanding the value of owing homes. Also, many people from the traditional style of doing task has now moved to digital mode such as virtual education, online medical consultations, webinars and online workshop etc, thereby increasing the need for house. This further has resulted in increase in demand for houses in several cities of India.

This research has used Theory of Planned Behaviour to elucidate the purchase behaviour of consumers during Covid-19 in Delhi -NCR, which would be useful for future engagement and prediction. In section 2, a detailed literature review on TPB, affordable housing and consumer buying behaviour has been done to analyse consumption pattern. Also, in section 3, the analysis of data is done using Structural equation modelling (SEM). Finally, in section 4 & 5, we present the output and conclude.

Literature Review

Consumer Buying Behaviour in Real Estate

“Consumer behaviour is the study of individuals, groups or organizations in obtaining, using and disposing of products and services, including the decision processes that precede and follow these behaviours” (Engel, Blackwell and Miniard, 1995).

Realty products are high involvement products and requires intensive research before decision is made. Therefore, buying a house is considered a complex buying process.

According to the model propounded by (Engel, et al., 1968) on consumer decision making, highlighting the psychological frame and individual behaviour from the time they have encountered the need for the product, quest for information and knowledge, alternative generation and evaluation, purchase of product and post evaluation. Also, it is assumed that the consumer purchase act is headed by mental process which involves cognitive process and function, to form attitude & belief about the product, emotional factor which develops the consumer negative or positive attitude. This development of attitude triggers the motivational drive of consumers to select and act. The attitude of consumer towards shopping product is different. The consumer attitude for high-involvement product is different as consumers engage intensively in search for information (Beatty and Smith, 1987; Punj and Staelin, 1983). For high involvement product like house, all LIG ad MIG consumers do cost benefits analysis, value analysis of the product they intend to search and buy (Urbany, 1986).

Demographic characteristics, beliefs and attitudes about product also affect consumer’s external search (Punj and Staelin, 1983; and Beatty and Smith, 1987). Real estate consumers acquire information from external sources brokers, newspaper, magazines and re-report for analysis and comparison of properties (Clark and Smith, 1979; Talarchek, 1982; and National Association of Realtors, 1989). There are consumers who have less knowledge / first time buyers / urban movers and they depend mainly on relatives and friends and tend to get influenced by their choices (Kaynak, 1985; and National Association of Realtors, 1990). “Experience may also affect what type of information is gathered” ( Bettman and Sujan, 1987). “Models of individual housing decision-making usually involve two or three stages: the decision to move, the selection of a destination and the selection of a particular home” (Brown and Moore, 1970; Speare and Wiseman, 1980). Engel et al. (1995) found that consumers make choice from the reduced set of alternatives or evoked set based on criteria while purchasing products. If criteria of two alternatives are same, then the decision is mostly based on price comparison. Dibb (1994) studied that consumer’s decision making while purchasing house is based on the principles of compensatory and non-compensatory rule. In non-compensatory rule, the consumer does not compromise on the principal factors such as – price, size and location. To narrow down the alternative’s consumer uses compensatory rule after non-compensatory rule to evaluate each individual unit before purchase.

Though, Consumer purchase behaviour for buying affordable house is affected by multiple factors, the key factors studied after extensive research from literature has been identified from Theory of Planned Behaviour (TPB)- attitude, subjective norms and purchase behaviour control which affects the purchase intention and purchase behaviour of consumers.

Theory of Planned Behaviour

As stated above that consumer behaviour is affected by many factors and Covid-19 is one such external factor which has impacted whole range of consumers in all dynamics of life. According to the theory shown in figure 1, behaviour is predicted by individual purchase intention & PI is the function of Attitude, PBC and Subjective norms.

Attitude – Attitude refers to the feeling, belief, or perceptions that an individual has towards something that influences the behaviour. It combines cognitive beliefs, purchase intention and emotional component (Engel et al., 1995). Mostly to guide behaviour, attitude relies on direct experience rather than indirect experience or gain knowledge about the house from other sources. Consumers acquire and search for more information if they are feeling less confident or are inexperienced to form attitude. Say, two consumers have to buy affordable house size 1000sqft may use similar attributes to evaluate choices but due to different belief, the satisfaction level may vary impacting different purchase intentions. Therefore, the most significant attributes impacting psychological and physical factors must be considered that affect consumer attitude. Thus, this clearly signifies that attitude has positive relationship with behaviour which can be seen in past studies too (George, 2002). Basis the information, the researchers intend to analyse whether:

H1. There is positive impact of attitude on the purchase intentions of consumers.

Subjective Norms “Subjective norms refers to the perceived social pressure to perform or not to perform the behaviour” (Azjen, 1991). Schepers (2007) defined subjective norms as “the pressure that a group exerts on individuals by altering their perceptions, opinions, attitudes and behaviour”. While purchasing house, consumer seek the approval from an important person or close associates or group of people for approval and support of the behaviour. Consumer purchase behaviour is mainly determined by people’s view, social pressure or reference groups. Generally, Subjective norms have a significant role in framing mind of an individual with positive or negative intentions.

According to Engel et al. (1995), Consumer uses reference group as a point of information to support their belief and attitude. However, reference group influences consumer through information termed as informational influence, norms as normative influence and values as value expressive influence. (Engel et al., 1995). “Reference groups are individuals or collections of people that a consumer uses as a point of comparison for attitudes, beliefs, values or behaviour” (Engel et al., 1995). Although, the family will always be the most influential reference group while purchasing a high involvement product (Igbaria & Schiffman, 1994).

Based on the information and argument, hypothesis 2 is framed.

H₂: There is positive impact of subjective norms on the purchase intentions of consumers

Perceived Behaviour Control

Based on TPB model, perceived behaviour control is defined as the degree of confidence that a consumer is capable to showcase while buying goods (Azjen, 1991), i.e., person’s control over his behaviour and his confidence in making choices. It is individual’s belief that the outcome of anything is determined by his/her own behaviour. Also, it is believed that people will buy a particular house when they believe that they have the resources and opportunities to make decisions. Though many researchers have found that there is a positive influence of purchase behaviour control but there are few who found insignificant relationship. This inconsistency is needed to be checked again. Hence the third hypothesis for our study is framed.

H₃: There is positive impact of perceived behaviour control on the purchase intentions of consumers.

Purchase Intention

It refers to the consumer attitude towards buying affordable house and the degree of willingness to pay. Consumer purchase intention increases with a known brand. Although, purchase intention is a variable which itself is dependent on multiple variables such as attitude, subjective norms, perceived behaviour control and all these variables have the ability to impace purchase behaviour of consumers. Consumer purchase intention though is a subjective matter towards buying affordable house but an important variable to predict purchase behaviour. Under unusual circumstances, such as Covid-19, the correlation between intention and action is non-existent (Chatzisarantis and Hagger, 2007). Due to this, the authors have thought to test:

H₄: Consumer purchase intention has an impact on the consumer purchase behaviour.

Affordable Housing

'Affordability', in itself is a subjective term and the definition will change from individual to individual Affordable housing is India is considered to be the focus area in today’s context (JLL 2012). Affordable housing demand far outweighs it’s supply , thereby highlighting the need for its study as there is a lot of scope in reducing the demand supply mismatch in this category considered ‘space consumption norms viz., housing unit sizes (or, built space)’ and ‘affordability metric’ to define affordable housing.

Therefore, the definition of affordable housing cannot be applied across different countries and the definition changes depending on the income levels of the people. The definition and scope of affordable housing is greatly contingent on a country/ region's level of economic development and income levels in Table 1.

Table 1 Affordable House, Source: Kpmg
Definitions of affordable housing in India (Developed by KPMG and CREDAI)
	Income Level	Size of Unit	Affordability
Economic Weaker Section (EWS)	< INR 150,000 per annum	Up to 300 sq. ft	EMI to  monthly income - 30 to 40%
Lower income group (LIG)	INR 150,000 to 300,000 per annum	Up to 300 sq. ft	EMI        to   monthly income - 30 to 40%
Middle income Group(MIG)	INR 300,000 to 1 million per annum	600 to 1200 sq. ft	House  price  to  annual income - less than 5.1x

Affordable housing model proposed by APSRCL (2014), Vastushodh (2014), IDFC (2012) and Monani (2014) also has helped to form the basis of the study.

Methodology

Research framework – The following figure 2 represent the research diagram used in the study to understand purchase behaviour of consumer towards affordable house during covid-19.

The present study attempts to use questionnaire research methods to determine the purchase behaviour of consumers in Figure 2. A quantitative approach is used to determine relationship amongst variables ( Creswell, 2013). The research has used survey questionnaire to test the hypothesis (De Vaus, 2002) & it also examines the relationship between the consumer’s purchase behaviour and purchase intention. The specific population studied termed as target population (Mann, 2007) for our study are the new home buyers or investors who are intending to purchase house during covid-19. The population studied are the people residing in Delhi-NCR. Convenience sampling technique is used to collect data from 250 participants and 200 respondents gave properly filled questionnaire with a response rate of 80% which is higher and is considered good for the study. The questionnaire administered to the participants were divided into 2 parts- (a) First part containing basic demographic information-age, gender, income, education, occupation and size of family (b) the second part of questionnaire contains all that information necessary to construct the latent variables. Further, 4 parameters for attitude, 3 parameters for Subjective norms, 4 parameters for Perceived behaviour, 3 parameters for purchase intention and 3 parameters for purchase behaviour have been studied by analysing data using structural equation modelling (SEM), AMOS 21.

To achieve model fit, exploratory and confirmatory factor analysis has been done to ensure that all variables studied are free from high correlation relationship. SEM allows to study the cause, direct effect and indirect effect as well as total effect of explanatory variables on each dependent item (Cao and Mokhtarian, 2005).

Findings

Demographic Characteristics- Table 2 represents the demographic characteristics of the respondents, consisting of 56.5%percentage of male and 43.5% of female. The respondents age ranges from around 25 to 55 years of age. All participants who so ever has been taken for the study is either looking for a new affordable house or looking to grab the opportunity as an investor. Almost all the respondents have the potential to purchase house. Majority of the respondents belonging to the middle-income segment and are intending to buy MIG flats. though, 23.5% of population are intending to buy LIG flats. The above demographic information is deemed fit for our study.

Table 2 Demographic Characteristics
Variable	Characteristics	Frequency	Percent
Age	< 30	45	22.5
	31- 35	55	27.5
	36-40	63	31.5
	> 40	37	18.5
Gender	Male	113	56.5
	Female	87	43.5
Profession	Business	63	31.5
	Service class	137	68.5
Income per annum	5-10 lakhs	47	23.5
	10-15 lakhs	64	32
	15-20 lakhs	35	17.5
	20-25 lakhs	28	14
	>25 lakhs	26	13

Reliability & Validity of the measurement scales

As per Tavakol and Dennick (2011), Pett et al. (2003) reliability and validity of 17 items out of 21 items have been analysed after PCA. Table 2 provides the detail of the items. All the items have passed the minimal threshold with respect to KMO test and Bartlett test of sphericity. Overall, the Cronbach’s alpha value for all the items studies is of 0.781, explaining 78.1% variance Table 3.

Table 3 Reliability And Validity Of The Measurement Scales
Variable	KMO Test	Bartlett Sphericity	Total explained variance	Cronbach's Alpha
Re-attitude	0.789	0.000	83.5	0.85
Re-subjective norms	0.872	0.000	75.2	0.83
Re-Perceived Behaviour control	0.851	0.000	78.4	0.87
Purchase Intention	0.812	0.000	82.34	0.8
Purchase Behaviour	0.74	0.000	72.45	0.79

Data Analysis & Hypothesis Testing

After verifying the reliability and validity of all the items, SEM tests the causal relationship of the variables taken for the study and to check uni-dimensionality of all the constructs before testing the hypothesis Table 4. Convergent validity and discriminant validity has been done using the formula given by (Fornell and Larcker, 1981).

Table 4 Description  Of Items
Description of attributes	Source	Items	RE-ATT	RE-SN	RE-PBC	RE-PI	RE-PB
During the ongoing pandemic, purchasing of affordable house is a good idea.	Taylor and Todd (1995)	AT1	0.82
During covid-19, purchasing house is safe and better proposition.		AT2	0.81
It is favourable time to purchase affordable house.		AT3	0.72
I take advantage of situation like Covid-19 while purchasing high involvement product.		AT4	0.74
All family members belief that I should take initiative in purchasing affordable house during this period.	Venkatesh (2000)	SN1		0.82
My friends and relative have some influence on my purchase specially when I purchase high involvement product		SN2		0.84
Many people associated with me believe that I should buy house during Covid-19		SN3		0.95
I am confident that I can purchase house whenever I want	Taylor and Todd (1995)	PBC1			0.88
I have money, authority and desire to purchase affordable house		PBC2			0.83
I am capable to differentiate between affordable house and a premium house		PBC3			0.8
It is most likely that I will have plenty of opportunities in future to purchase house		PBC4			0.77
My intention to buy affordable house will be higher than the premium house	Fu et.al. (2016)	PI1				0.87
I intend to purchase affordable house because of price.		PI2				0.93
I will consider buying affordable house for sustainability reasons		PI3				0.74
I will deliberately purchase affordable house for better deal.	De canniere et al. (2009)	PB1					0.83
I will keep preference whenever I have an option to purchase affordable house.		PB2					0.84
Choosing affordable house is something that happens automatically		PB3					0.81
Sum of factor loading		λ	3.09	2.61	3.28	2.54	2.48
Average variance extracted		AVE	0.7725	0.6525	0.82	0.635	0.62
Composite/ Construct Reliability		CR	0.92	0.94	0.95	0.93	0.92

To validate measurement model, convergent validity, composite reliability and divergent validity is analyzed based on average variance extracted (AVE), factor loading and composite reliability. The values analyzed for Re-Attitude, Re-Subjective Norms, RE-Purchase Behavior Control, RE-Purchase intention and Re-Purchase Behavior is greater than the threshold value of 0.5 which validates the convergent validity. Also, the factor loading of all items observed must be greater than 0.5. This states that all the variables confirm to the convergent validity test.For construct validity, construct reliability (CR) must be greater than 0.5 (Bagozzi and Yi, 1988). Table 5 represents the convergent validity. In this study. The result of average variance extracted (AVE), factor loading, and composite reliability is shown in Table 4 & it supports convergent validity. GFI result support construct validity . As for composite reliability (CR), the factor loading must be greater than the recommended value 0.7 for all the items of latent exogeneous construct.

Table 5 Absolute And Incremental Indices
Absolute and Incremental indices
Fit Indices	Detected Values
X2/df (Chi sqr/degree of freedom)	1.356
GFI (Goodness of fit indices)	0.93
CFI (Comparative fit indices	0.95
RMSEA (Badness of fit indices)	0.03
Std RMR (Root mean residual)	0.033

Based on this, measurement model has been developed to support convergent validity shown in figure 3. Maximum likelihood model has been used to test the model estimation. The goodness of fit indices = 0.93, CFI=0.95, RMSEA=0.03, RMR=0.033 and CMIN/ df=1.356, all the values has been taken with minimum acceptability threshold of the indices. The overall analysis is shown in Table 5.

As final outcome, the revised model has been achieved and 2 Hypothesis are accepted for instance Attitude (H1) of consumers in real estate at β=0.075, CR=2.6818, p<0.05 and also Purchase Intention (H4) at β=0.174, CR=2.0423, p<0.05 showed that Attitude (H1) and has a significant impact on the Purchase intention, and Purchase intention (H4) has a significant impact on the consumer purchase behaviour. While Subjective norm (H2) at β =- 0.14, CR=1 .0682, p=0.252 and perceived behaviour control (PBC) at β=-0.104, CR= - 0.518, p=0.21 do not have significant impact on consumer purchase intention during covid-19. The regression analysis &hypothesis result Table 6.

Table 6 Hypothesis Test & Revised Model
Regression Analysis and Hypothesis results
Hypothesis	Construct	Estimate	S.E	CR	P	Hypothesis results
H1	Attitude > CPI	0.177	0.066	2.6818	***	Supported
H2	Subjective Norms > CPI	-0.141	0.132	-1.0682	0.252	Not Supported
H3	Perceived Behaviour control > CPI	-0.014	0.027	-0.5185	0.321	Not Supported
H4	CPI > CPB	0.145	0.071	2.0423	***	Supported

Conclusion

The present research tried to study the impact of Covid19 on the purchase behavior of consumers. The research aimed at analyzing the consumer purchase behavior specially during this pandemic. For this, a well-designed framework of TPB model has been used in understanding purchase intention and behaviour of consumers. Overall, the hypothesized model and the revised model has been analyzed from the study using SEM. Though, hypothesized model states 3 constructs have direct bearing on Purchase intention and Purchase Intention have direct bearing on Consumer purchase behaviour. While revised model states that 2 constructs have direct impact on consumer purchase behaviour. Though, other construct may have impact but due to p value >0.05, the result could not be generalized. From the hypothesis table, it can be deduced that Attitude of respondents have direct impact on consumer purchase intention and therefore hypothesis (H1) is accepted. Similarly, consumer purchase intention impacts consumer purchase behaviour and the hypothesis H4 is accepted. Alternatively, the two paths from hypothesis H2, H3 have been consistently insignificant for P-value>0.05 in SEM and hence H2, H3 is rejected.

The outcome of the research is certainly adding value to the literature and understanding consumer purchase behaviour during Covid-19 will help companies to design product and strategy in relation to the need of the consumer. The result certainly indicates that consumer attitude towards the product must be positive, as long as consumer attitude is positive; it will keep impacting purchase intention which in turn would impact purchase behaviour. Therefore, it is important that organization and system must develop policies that encourage consumers to buy their own affordable house at a reasonable price for a happy life.

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