Apartment Purchase Decision Variables in Real Estate Companies: Sales Teams Perspective in Addis Ababa, Ethiopia

This study intended to assess factors affecting apartment purchase decision of consumers with respect to real estate sales team perspective in Addis Ababa using four major factors that would affect the purchase decision of a consumer viz., price, location, quality of apartment and corporate image. Accordingly, the study used both descriptive and explanatory research design. Self-administered structured questionnaires were used to collect primary data from a total sample size of 50 sales experts or sales/marketing manager of real estate companies who are drawn using convenient sampling. The relationships proposed in the framework were tested using Pearson correlation, and the causal relations were analyzed using regression analysis. Accordingly, it is found out that there is a strong significant relationship between purchase decision factors and purchase decision with Pearson correlation coefficient of 0.699 (r=0. 699) . In addition, purchase decision factors have an influence on purchase decision in which 52.20% of the variation in purchase decision is explained by the variation in price, location, quality of apartment and corporate image. Furthermore, corporate image is the most influencing apartment purchase decision factor followed by quality of apartment. Thus, it is recommended that real estate companies may give top priority in building their corporate image and developing quality apartments as these two factors are highly influencing real estate customers purchase decision.


MATERIAL AND METHODS
The study focuses on selected real estate companies in Addis Ababa. There 156 active real estate companies found in Addis Ababa which are involved in building and selling of Apartments, Villas and commercial centers. These real estate companies sell their product by different ways. Some of them have well organized marketing while others sell their products through outsourced marketing department. The study adopted quantitative research approach and a cross-sectional-descriptive and explanatory research design. Primary data was collected from respondents using Self-administered structured questionnaire using a 5 point Likert scale. All active Real estates in Addis Ababa were considered as the target populations of the study. By using the Carvalho (1984) sample size determination table, the largest sample size of 50 active Real estates were considered as sample size of the study and the sample were drawn using simple random sampling technique and one sales person or Sales/Marketing managers from each real estate companies were drawn using convenient sampling technique. The data was analyzed using descriptive statistics (such as percentage, frequency, mean and standard deviation) and inferential statistics such as correlation and regression analysis was employed.
The standard questionnaire that was used to collect the necessary information regarding the study was adapted and modified from the work of Aschalew Dessie (2018) and Genet G/Meskel (2019).
The internal consistency/reliabilities of purchase decision factors and purchase decision was assessed with overall Cronbach's Alpha of 0.811 and the reliability values for all constructs was confirmed as greater than 0.7, which are considered reliable in general (Cohen et al., 2007). With respect to ethical consideration, participants were not exposed to physical or psychological harm; participants were participated only on a voluntary basis, privacy of respondents was respected and findings were reported in a complete and honest fashion, unanimously.

MAJOR FINDINGS 4.1. Socio-Demographic Information
The demographic profile of the sample respondents is presented below in Table 1. Most of the respondents (66.0%) were sales person, while remaining, 34.0% were sales\marketing managers. While, about 82% the respondent's age found to be between 18-39 years. Similarly, about 82% of the respondents were found to have educational background of first degree and above. With regard to respondents' year of experience in the real estate industry, 60% of respondents had working experience of 1-5 years.

Analysis of Purchase decision factors
In descriptive data analysis, averages (mean) were calculated for each construct in the Likert Scales, from Strongly Disagree=1 to Strongly Agree=5. The numbers entered into the SPSS version 24 thus represented the weight and the weighted averages for the scales were calculated to understand the mean values. This was accomplished by dividing the distances between the scale values (4 in a 5-point Likert Scale by the number of values (5). Thus, the period length is 4/5=0.80, which is used to calculate the weighted averages (Alfarra, W.A., 2009).
The weighted average categories (mean value) is interpreted with the degree of agreement for each factor calculated accordingly and its interpretation was made on the basis of Alfarra, W.A., (2009) suggestion as Weighted Average between 1.00-1.79 interpreted as very un influential, 1.80-2.59 as un influential, 2.60-3.39 as Neutral/do not know, 3.40-4.19 as influential and 4.20-5.00 as very influential (please refer   .047 .001 .000 .000 *. Correlation is significant at the 0.01 level (2-tailed). Source: Research data (2020)

The role of Price on purchase decision
To measure Real estate purchase decision factors related with price (P), five items were developed in this research. Table 4 shows the level of relationship that exists between price and Apartment purchase decision. Accordingly, the group mean of Price was 3.472 and it indicated average/ moderate performance level with respect to the overall measures taken into consideration. The results are presented on table 4.2 below as follows: From the above table it can be noted that the majority of the sales team agree that customers are price sensitive and usually they have high sales volume when there is attractive payment schedule, low first payment and when apartment price is low with a mean score of 4.24, 4.20 and 4.18 for attractive payment schedule, low first installment and low apartment price respectively. From the above results majority of respondents do not agree in that they have high sales volume when first installment is high and when apartment price is high with a mean score of 2.42 and 2.32 respectively. This would mean that most of the respondents agreed on developing good pricing strategy have significant influence on the purchase decision with a group mean of 3.472. Thus this result implies that it is influential according to weighted average results. Purchase decision increases as price of an apartment is low and when first installment is low. On the other hand, purchase decision decreases as price of apartment increases and when first installment is high.
The result obtained from this study is similar with a study conducted in Malaysia in which price is one of the factors affecting apartment purchase decision (Rachmawati et al., 2019).The result obtained from this study is also similar with a study conducted in Addis Ababa which found price as a factor affecting apartment purchase decision( G/Meskel.,2019). Similar result was obtained from this study as that of Dessie who tried to elaborate a significant association between purchase decision and price (Dessie, 2018).
Another study conducted in Saudi Arabia shows similar result with results obtained from this study that purchase of apartment is influenced by price (AL-Nahdi et al., 2015). The result obtained from this study is also similar with a study conducted in China which found Price as influencing factor in purchase of apartments(Juncheng-Zhao, 2019). ISSN 2422-8451 An International Peer-reviewed Journal Vol.79, 2021 16 The result obtained from this study revealed that price of the apartment with group mean value of 3.472 and influential on purchase decision which is also similar to a study conducted in India that found price influential on purchase decision with mean value of 4.00 (Manoj and Nasar, 2014). Price is positively related to Purchase decision with a Pearson correlation coefficient of 0. 282 (r=0. 282) and significance value of less than 0.001. This significance tells that there is weak positive relationship between price and Purchase decision.

The role of Location on purchase decision
To study Apartment purchase decision factors concerning location, five items were developed in this research. The results are presented on table 4.3 below: Based on the result majority of respondents agreed that apartment buyers prefer apartments located at available infrastructure in the neighborhood, apartments located at convenient place for transportation, availability of health facilities and other public facilities (recreational centers, shopping centers and other facilities), apartments not exposed to noise and apartments at the down town with a mean score of 4.34, 4.16, 4.16, 4.02 and 4.00 respectively.
From the above results majority of respondents agree location convenience is one of the factors for apartment purchase decision by customer with a group mean score of around 4.136 and this shows location is influential on purchase decision based on weighted average result analysis. This result is similar with a study conducted by Dr, Manoj and Nasar who found location of apartment as one of influencing factor of purchase decision with mean value of 3.72(Dr. Manoj and Nasar, 2014). The result obtained from this study is similar with a study conducted in Addis Ababa by G/Meskel in which location is one of the factors that influence purchase decision (G/Meskel, 2019). It is found that the result obtained from this study is also similar with a study conducted by Dessie who stated location as one of the factors influencing purchase decision (Dessie, 2018). The result obtained from this study is also similar with study conducted in Malaysia (Rachmawati et al., 2019). The result obtained from correlation analysis found weak positive relationship between location and Purchase decision with a Pearson correlation coefficient of 0. 474 (r=0.474) and significance value is less than 0.001.

The role of Quality of Apartment on purchase decision
According to the results obtained from this study, majority of the respondents agreed with regard to quality of construction material (mean score of 4.44) which is the highest apartment purchase decision factor with respect to quality of apartment. Among the items listed for quality of apartment the use of up-to-date technological facilities scored the minimum mean (3.74). In general, quality of apartments had significance influence on purchase decision with group mean value 4.0480.
The result obtained from this study is similar with study conducted in Malaysia which found quality factor as influential to the purchasing decision. (Mariadas et al., 2019).
The result obtained from this study is similar with a study conducted by G/Meskel in which quality of apartment is one of the factors that influence purchase decision (G/Meskel, 2019). It is also found that the result obtained from this study is similar with a study conducted by Dessie who elaborated quality of apartment as one of the factors influencing purchase decision (Dessie, 2018). Similar result was obtained from this study with study conducted in Malaysia (Rachmawati et al., 2019).
The result obtained from this study found group mean score of 4.05 which shows location is influential on purchase decision based on weighted average result analysis. This result is similar with a study conducted by Manoj and Nasar who found quality of apartment as one of influencing factor of purchase decision with mean value of 3.38 (Manoj and Nasar, 2014).
There is strong positive correlation between quality of apartment and purchase decision with a Pearson correlation coefficient of 0.660 (r=0.660) and significance value of less than 0.001. Quality of apartment is found to be the second most significant Purchase decision factor dimension next to corporate image with Beta value of 0.302 implying that this dimension is significantly related and strongly influence Purchase decision which is different from a study conducted in India that revealed price of the apartment is most influencing attribute (Manoj and Nasar, 2014).

The role of Corporate Image on purchase decision
Aspects like reliability, Delivery time, Strong financial capability, engaging in multiple businesses, engaging only in real estate business, marketing performance are items included while investigating corporate image regarding apartment purchase decision factors. The results are presented on table 4 below: As indicated in table 4.5 above, reliability of a real estate company is the highest score with mean value of 4.44. The majority of the respondents agree on the items listed for corporate image with regard to apartment purchase decision factors with group mean value of 4.0767. Thus this result implies that it is influential according to weighted average results.
The result obtained from this study is similar with a study conducted in Malaysia in which corporate image as one of the factors affecting apartment purchase decision (Rachmawati et al., 2019).The result obtained from this study is also similar with a study conducted in Addis Ababa which found corporate image as a factor affecting apartment purchase decision (Dessie, 2018). Another study conducted in India shows similar result with results obtained from this study that purchase of apartment is influenced by corporate image (Manoj and Nasar, 2015). The result obtained from this study revealed that corporate image with group mean value of 4.0767 and influential on purchase decision which is also similar to a study conducted in India that found corporate image influential on purchase decision with mean value of 3.60 (Manoj and Nasar, 2014). There is strong positive relation between corporate image and purchase decision with correlation coefficient of 0.705 (r=0.705) and significance value less than 0.001.

Regression Analysis
The regression analysis was conducted to know by how much the independent variable explains the dependent variable. The regression was done between purchase decision factor constructs (independent variable) and Purchase decision (dependent variable). With this Multiple Linear Regression model, the p-value ("sig" for significance") of the predictor's effect on the criterion variable, if less than 0.05 is generally considered "statistically significant". The model specification is as follows: Multiple Linear Regression model: Y = β0 + β1 X1 + β2 X2 + β3 X3 + β4 X4 + є where Y = Purchase Decision; β0= the y intercept when x is zero; β1, β2, β3, β4, are regression coefficients of the following variables respectively; x1-Price; x2-Location; x3-Quality of Apartment; x4-Corporate Image; є is the error term. Therefore, based on this model the results of the regression analysis are presented as follows:  (2020) The result in table 4.9 shows that the co linearity between independent variables has no series problem. Since the value of tolerance for all independent variable is greater than 0.1 and all VIF is less than ten (VIF<10). a) Normality test The normality test can be done in different ways. This study tried to test the normality using histograms, normal Q-Q plots, skewness and kurtosis. The visual inspection of Histograms and Normal QQ plots showed the data is normally distributed. Dividing the skewness and kurtosis results by their respective standard error will give Z value. Z value should be between -1.96 and +1.96. Accordingly Z value for skewness was found -0.629(-0.212/0.337) and Z value for kurtosis was found -1.124(-0.744/0.662). Since Z value is within the range, the model is found normal and the data is approximately normally distributed. b) Homoscedasticity Scatter plot of regression standardized residual versus regression standardized predicted values obtained from the study result showed that it does not have an obvious pattern, there are points equally distributed above and below zero on the X axis, and to the left and right of zero on the Y axis. This shows the existence of homoscedasticity. As it has been stipulated on the table 4.9 above, the significant and positive β coefficient implies that Purchase decision factors have a positive influence on Purchase decision. The coefficient of determination, adjusted R2 is .522, meaning that 52.20% of the variation in Purchase Decision is explained by the variation in Price, Location, Quality of apartment and Corporate image. This shows there is causal relationship between Purchase decision factors and Purchase decision. It is believed that increasing the number of independent variables increases the coefficient of determination. This study used only four independent variables due to scarce resource, time and other factors. The remaining 47.80% of the variation in Purchase Decision cannot be explained by the above dimensions of Purchase Decision factors. Therefore, there are other additional factors that can explain the variability in this variable. These factors might be other factors that can't be managed by the company such as culture, opinion of others, personal factors and psychological factors. This implies the multi-dimensionality of Purchase Decision factors which covers set of activities and processes from real estate companies' internal operations to upstream and downstream sides and external factors to achieve the ultimate common goal of improving purchase decision. c) ANOVA Test  (2020) This study used ANOVA to determine the significance of the regression model from which an Fsignificance value of p<0.001 was established. This shows that the regression model has a less than 0.001 likelihood (probability) of giving a wrong prediction. Hence, from the table 4.11 above, the regression model is overall statistically significant, meaning that it is a suitable prediction model for explaining how Purchase decision factors affects Purchase decision.  (2020) On the above table the beta values show that the magnitude of influence between variables, higher values being an indication of strong influence. From the above table 4.12 coefficients results, the following regression analysis was obtained: Y = 0.961+ 0.302X3 + 0.463X4 + є

Coefficient Results
The model shows that when all variables are held at zero (constant), the value of Purchase decision would be 0.961. But when holding other factors constant, a unit increase in level of Quality of Apartment would lead to a 0.302 increase in Purchase decision, and a unit increase in corporate image would lead to a 0.463 increase in Purchase decision.
In this study, corporate image had highest Beta coefficient of 0.463. This result implies that corporate image has highest impact on Purchase decision. Whereas, Quality of apartment is found to be the second most significant Purchase decision factor dimension with Beta value of 0.302 implying that this dimension is significantly related and strongly influence Purchase decision. Then the influence on Purchase decision is followed by Price and Location with Beta value of 0.052 and 0.044 respectively which shows price and location are almost insignificant in affecting purchase decision.
This study found corporate image as the most influencing purchase decision factor followed by quality of apartment, location and price respectively which is different from a study conducted in India that revealed price of the apartment is most influencing attribute for the customer perspective to purchase the real estate residential apartment(Dr Manoj and Nasar,2014). Based the relationship between the independent variables and dependent variable this study also found corporate image as the most influencing purchase decision factor followed by quality of apartment, location and price respectively which is opposed to study conducted in Malaysia which found that location as the most important factor followed by price, quality, corporate image on customer purchase decision (Rachmawati et al., 2019).

CONCLUSION and RECOMMENDATION
The study concludes that there is a significantly positive relationship between purchase decision factors and purchase decision as it is proved that price and location has little effect on purchase decision where as corporate image is the most influencing factor on apartment purchase decision followed by quality of apartment. Hence, it is recommended that real estate companies should focus on building their image and increasing the quality of the apartments constructed.