Factors that influence buying houses for consumers

lifestyle factors on consumer decisions. This research was conducted at the Royal Spring Housing, located in Gowa Regency. The population chosen is all home consumers at the Royal Spring Housing PT. Buana Sentosa Asri in Makassar. The number of samples is calculated using the Slovin formula, and the number of samples to be studied is 82 household samples and is determined by simple random sampling. Data collection was carried out by distributing questionnaires to all respondents. The analysis tool uses multiple linear regression. The results of the study found that family factors, economic factors, and lifestyle have a positive and significant effect on consumer decisions. INTRODUCTION The property sector is an alternative that is in great demand by consumers (Salam & Chandra, 2019). Land prices tend to increase from year to year due to the limited amount of land. Simultaneously, the demand for housing is higher, and the number of residents increases, making the property sector increase in investment activities (Wijayanti & Srihandayani, 2015). Modern society is more likely to need a house with various facilities such as sports facilities, recreation, and guaranteed security in an area with a one-door system as access in and out. Makassar City is one of the centers of economic growth reasonably high population growth rate in Indonesia. Currently housing in Indonesia is still a polemic because it's not easy and easy to buy a house. Consumers will seek further information internally and externally until a purchase decision is reached (Wijayanti & Srihandayani, 2015). Community developments and current government policies cannot be separated from the problems of housing and settlement. This phenomenon represents a significant potential for residential property companies. However, on the other hand, competition is growing stronger because the producers of each commodity or service must take advantage of all their potential and opportunities (Suryawan, 2019). Family factors influence a person's behavior significantly, especially those related to the use of products. As novice users of a product, consumers certainly have limited information about the Jurnal Manajemen Bisnis Vol. 8, No.1 (2021) Maret, Hal. 57-66 e-ISSN: 2621-1971|p-ISSN: 2088-7086 Factors that influence buying houses for consumers 58 use of a product; the family offers suggestions or references to family members about a product to use. Consumers choose families as their primary source of information because of the trust, ease of access and experience other family members have endeavored to prevent consumers from wasting time asking people with no experience (Ghasarma & Daud, 2014). Meeting the needs of housing that can be reached by the middle to the lower-income community is considered urgent at this time due to population growth (Waha & Sondakh, 2014). This condition promises a housing business opportunity in Makassar and creates intense competition between development companies. Carefulness to seize existing opportunities is needed so that companies can survive in line with changing times. Therefore, winning an industrial battle requires accurate marketing strategies (Listyorini, 2012). Consumer behavior analysis is directly related to the activities of individuals in the production and use of goods and services such as the decision-making process, preparation and determination of marketing measures (Latief & Ayustira, 2020; Basalamah et al., 2020). Kotler (2010) suggests that marketing is a business activity system for distributing goods that are capable of satisfying desires and achieving target markets and corporate goals. This illustrates that the purposes and principles of the marketing are not only sales but also socially oriented and consumer satisfaction and desires through the exchange process for the profit of the company. Understanding consumer behavior is certainly not easy because the characteristics of consumers and unlimited human needs. More and more selective consumers when choosing to buy a home warn property contractors about efforts to satisfy consumers when they buy a home (Wijaya, 2018). Companies, on the other hand, must win the competition. The company therefore needs a fixed marketing strategy for its products. In addition Kotler, (2010) states that a product is offered to the market to meet needs and wishes, to be purchased, used or consumed. By seeking certain benefits from a product, consumers will try to satisfy a demand. Consumers shall see the product as a collection of specific characteristics or characteristics with different abilities to provide and meet the services sought. Dynamic environmental changes have led to consumer lifestyle studies that allow marketers to understand how consumers think and select alternatives (Sumarwan, & Yuliati, 2015). A marketing life style perspective shows the classification of individuals into a group based on what they do, how they spend their time and how they use their income (Listyorini, 2012). External and internal conditions directly affect consumer behaviour. External factors include culture, subculture, social class, social groups, benchmarks and families. In the meantime, internal factors include learning, personality and self-conception, and attitudes in consumers themselves (Nasir, 2015). This lifestyle towards consumer choices was found in the results of the study (Kaharu & Budiarti, 2016). The higher the lifestyle of the consumer during shopping, the more the purchasing decision will increase. Style of life can influence buying, changing habits, tastes and buying behaviour. Low or high lifestyles have an impact on buying choices. Satisfaction and decisions on home purchases are very important in the housing development industry (Lolowang, 2019). However, the quality of the houses offered has still been disappointed by consumers. Before occupation there are often damages, protracted damages, Jurnal Manajemen Bisnis Vol. 8, No.1 (2021) Maret, Hal. 57-66 e-ISSN: 2621-1971|p-ISSN: 2088-7086 Factors that influence buying houses for consumers 59 improper construction of water canals may cause flooding, and when it rains, the roof often leaks due to unskilled tile (Nainggolan & Heryenzus, 2018). Companies must therefore recognize and know what consumer needs and desires are (Nainggolan & Heryenzus, 2018). The factors affecting consumer behaviors, including families, economic circumstances and lifestyles, must be assessed in the conduct of consumer behavior analyzes of marketed products, namely housing products in Makassar. These factors are very important in the marketing of the company's products. The analysis of consumer behavior aims to boost sales and should focus on changes in the company, monitor consumer behavior and efforts to improve products and better services at affordable prices. The authors therefore feel the need to investigate consumer behavior in relation to home purchases as basic human needs. This research research was conducted in the PT Buana Sentosa Asri Royal Spring Housing in Makassar. This research should know what factors make home decisions for consumers. These factors are confined to family factors, economic conditions and lifestyles in the limits and scope of the study. The effects of each element will be recognized through multiple linear regression analyses and the factors dominating the consumer's decision-making. will then draw up the recommendations for a solution to the problem. The hypothesis we suggest in this study is: H1 : Family has a positive and significant effect on consumer decisions. H2 : Economic factors have a positive and significant effect on consumer decisions. H3 : Lifestyle has a positive and significant effect on consumer decisions. RESEARCH DESIGN This research was carried out at Gowa Regency's Royal Spring Housing. The chosen population is the home of the Royal Spring Housing PT Buana Sentosa Asri in Makassar. The number of samples calculated using the Slovin formulation is 82 household samples and is determined by simple random samples. The collection of data was carried out through the distribution of questionnaires to all respondents. This data analysis uses descriptive analyses, validity of tests and reliability tests, regression analyses to assess the impact of the independent variables on the dependent variable (family, economic conditions, lifestyle) (consumer purchasing decisions). Multiple equation of linear regression with three independent variables, namely: Y = b0 + b1x1 + b2x2 + b3x3 + e Keterangan: Y = Consumer Decisions b0 = Constant Value X1 = Family factors X2 = Economic factors X3 = Lifestyle factors b1, b2, = Regression Coefficient Jurnal Manajemen Bisnis Vol. 8, No.1 (2021) Maret, Hal. 57-66 e-ISSN: 2621-1971|p-ISSN: 2088-7086 Factors that influence buying houses for consumers 60 Furthermore, the F-count test (simultaneous test) is carried out to see simultaneously whether there is a significant positive effect of the independent variables (X1, X2, X3), namely family, economic situation, lifestyle on consumer purchasing arrangements. The F-count value will be compared with the F-table value. The decision-making criterion is H0 is accepted if F count <F table at α = 5%. Ha is accepted if F count> F table at α = 5%. The t-test aims to partially assess whether family variables, economic circumstances and consumer affectly to purchasing lifestyle decisions. The criterion for decision-making is H0 if ttable>t-count at α = 5%. Ha is born when t count <t table or t count>t table α = 5%. The determinant is used to measure the influence of the studied independent variables (X1, X2, X3): family, economic and lifestyle variables on consumer buying decisions (Y), the dependent variable. The determinant coefficient (R2) ranges from zero to one (0 to R2 to 1, respectively). This means that R2 = 0 does not show the independent variable's influence on the dependent variable. RESULT AND DISCUSSION RESULT Data collection in this study was conducted through the distribution of questionnaires to respondents, namely consumers or homeowners in the Royal Spring Housing, through visits to the Royal Spring Housing and through the provision of questionnaires to homeowners who met and were willing to respond. The results of the validity test show that all question items with variables of family factors, economic factors, lifestyle factors and purchasing choices are valid and can be used as a tool for research measurements. This is demonstrated by the corrected item Total > 0.217 value. Reliability tests show how much a data collection tool can be trusted and used. The greater the device's reliability, the more reliable the measurement results are. The more trustworthy an instrument is, the agency obtains the same results if it measures the same object several times. The reliability method often used is Cronbach Alpha, which shows how consistent respondents respond to the instruments being evaluated. A research tool is said to be trustworthy if the value is > 0.60. The reliability of the research instrument is determined when the alpha of Cronbach is < 0.6, when reliability is considered to be low if the alpha of Cronbach is 0.6 – 0.77, then reliability is considered to be sufficient, and when the Alpha of Cronbach > 0.8, the reliability is said to be good. Table 1. Reliability Test Results Variable Koefisien Alpha Info Family factors 0,811 > 0,601 Reliable Economic factors 0,801 > 0,601 Reliable Lifestyle factors 0,781 > 0,601 Reliabel Consumer Decisions 0,791 > 0,601 Reliabel Table 1 shows that all variables used as instruments in the study are reliable and can be used as tools for data collection. Based on the results of the reliability test, it shows that the instrument Jurnal Manajemen Bisnis Vol. 8, No.1 (2021) Maret, Hal. 57-66 e-ISSN: 2621-1971|p-ISSN: 2088-7086 Factors that influence buying houses for consumers 61 has a very high degree of reliability; this is demonstrated by the alpha> 601 coefficient value so that measuring results can be trusted. The normality test is carried out to see whether, in the regression model, the dependent variable and the independent variable both have a normal distribution or not. A good regression model is a regression model that is usually distributed. How to detect normality is done by looking at the graph histogram. Figure 1. Histogram Graph The histogram graph in Figure 1 shows that the histogram graph provides a distribution pattern near normal. This is shown by the fact that the map is symmetrical and follows a diagonal line. This histogram is not precise, however, particularly if the number of samples used is small. The standard probability plot is a reliable method. In the standard graph, the points around the diagonal line are dispersed, and the distribution follows the diagonal line direction. Figure 2. Normal Prabability Plot On the basis of Figure 2, the expected probability plot is shown that the point spreads around the diagonal line and the distribution is following the diagonal line, so the distribution pattern can Jurnal Manajemen Bisnis Vol. 8, No.1 (2021) Maret, Hal. 57-66 e-ISSN: 2621-1971|p-ISSN: 2088-7086 Factors that influence buying houses for consumers 62 be said to be typical. Looking at the two graphs above, the regression model can be concluded that in this study it meets the assumption of normality. Heteroscedasticity indicates that the variance of the variables is not the same for all observations. If the residual variance from one word to another is constant, it is called homoscedasticity. A good regression model is homoscedastic or does not occur Heteroscedasticity. Because the cross-section data have data representing various sizes (small, medium, and large). To detect Heteroscedasticity, the method used is the chart method (diagram Scatterplot). If there is a specific pattern registered with dots, which form a particular regular way (wavy, widened, then narrowed), Heteroscedasticity occurs. Figure 3. Scatterplot diagram Figure 3 shows that the data is randomly dispersed and does not form a particular pattern; this indicates that no heterocedasticity is present. Therefore, it can be concluded that the variance of the residuals from one observation to another is different. Multicollinearity test aimed at testing the correlation between the independent variable (independent) in the regression model. In a good regression model, there should be no correlation between variables. To test the presence or absence of multicollinearity in the regression model, it can be seen from the value tolerance and its counterpart, namely by looking at the variance inflation factor (VIF). A commonly used cut-off value is a value of patience 0.01. One way to test for multicollinearity can be seen from the Variance Inflation Factor (VIF). If the VIF value> 10, then multicollinearity occurs Table 2. Multicollinearity Test Results


INTRODUCTION
The property sector is an alternative that is in great demand by consumers (Salam & Chandra, 2019). Land prices tend to increase from year to year due to the limited amount of land. Simultaneously, the demand for housing is higher, and the number of residents increases, making the property sector increase in investment activities (Wijayanti & Srihandayani, 2015). Modern society is more likely to need a house with various facilities such as sports facilities, recreation, and guaranteed security in an area with a one-door system as access in and out. Makassar City is one of the centers of economic growth reasonably high population growth rate in Indonesia.
Currently housing in Indonesia is still a polemic because it's not easy and easy to buy a house. Consumers will seek further information internally and externally until a purchase decision is reached (Wijayanti & Srihandayani, 2015). Community developments and current government policies cannot be separated from the problems of housing and settlement. This phenomenon represents a significant potential for residential property companies. However, on the other hand, competition is growing stronger because the producers of each commodity or service must take advantage of all their potential and opportunities (Suryawan, 2019).
Family factors influence a person's behavior significantly, especially those related to the use of products. As novice users of a product, consumers certainly have limited information about the use of a product; the family offers suggestions or references to family members about a product to use. Consumers choose families as their primary source of information because of the trust, ease of access and experience other family members have endeavored to prevent consumers from wasting time asking people with no experience (Ghasarma & Daud, 2014).
Meeting the needs of housing that can be reached by the middle to the lower-income community is considered urgent at this time due to population growth (Waha & Sondakh, 2014). This condition promises a housing business opportunity in Makassar and creates intense competition between development companies. Carefulness to seize existing opportunities is needed so that companies can survive in line with changing times. Therefore, winning an industrial battle requires accurate marketing strategies (Listyorini, 2012).
Consumer behavior analysis is directly related to the activities of individuals in the production and use of goods and services such as the decision-making process, preparation and determination of marketing measures (Latief & Ayustira, 2020;Basalamah et al., 2020). Kotler (2010) suggests that marketing is a business activity system for distributing goods that are capable of satisfying desires and achieving target markets and corporate goals. This illustrates that the purposes and principles of the marketing are not only sales but also socially oriented and consumer satisfaction and desires through the exchange process for the profit of the company.
Understanding consumer behavior is certainly not easy because the characteristics of consumers and unlimited human needs. More and more selective consumers when choosing to buy a home warn property contractors about efforts to satisfy consumers when they buy a home (Wijaya, 2018). Companies, on the other hand, must win the competition. The company therefore needs a fixed marketing strategy for its products. In addition Kotler, (2010) states that a product is offered to the market to meet needs and wishes, to be purchased, used or consumed. By seeking certain benefits from a product, consumers will try to satisfy a demand. Consumers shall see the product as a collection of specific characteristics or characteristics with different abilities to provide and meet the services sought.
Dynamic environmental changes have led to consumer lifestyle studies that allow marketers to understand how consumers think and select alternatives (Sumarwan, & Yuliati, 2015). A marketing life style perspective shows the classification of individuals into a group based on what they do, how they spend their time and how they use their income (Listyorini, 2012). External and internal conditions directly affect consumer behaviour. External factors include culture, subculture, social class, social groups, benchmarks and families. In the meantime, internal factors include learning, personality and self-conception, and attitudes in consumers themselves (Nasir, 2015). This lifestyle towards consumer choices was found in the results of the study (Kaharu & Budiarti, 2016). The higher the lifestyle of the consumer during shopping, the more the purchasing decision will increase. Style of life can influence buying, changing habits, tastes and buying behaviour. Low or high lifestyles have an impact on buying choices.
Satisfaction and decisions on home purchases are very important in the housing development industry (Lolowang, 2019). However, the quality of the houses offered has still been disappointed by consumers. Before occupation there are often damages, protracted damages, improper construction of water canals may cause flooding, and when it rains, the roof often leaks due to unskilled tile (Nainggolan & Heryenzus, 2018). Companies must therefore recognize and know what consumer needs and desires are (Nainggolan & Heryenzus, 2018).
The factors affecting consumer behaviors, including families, economic circumstances and lifestyles, must be assessed in the conduct of consumer behavior analyzes of marketed products, namely housing products in Makassar. These factors are very important in the marketing of the company's products. The analysis of consumer behavior aims to boost sales and should focus on changes in the company, monitor consumer behavior and efforts to improve products and better services at affordable prices. The authors therefore feel the need to investigate consumer behavior in relation to home purchases as basic human needs.
This research research was conducted in the PT Buana Sentosa Asri Royal Spring Housing in Makassar. This research should know what factors make home decisions for consumers. These factors are confined to family factors, economic conditions and lifestyles in the limits and scope of the study. The effects of each element will be recognized through multiple linear regression analyses and the factors dominating the consumer's decision-making. will then draw up the recommendations for a solution to the problem. The hypothesis we suggest in this study is: H1 : Family has a positive and significant effect on consumer decisions. H2 : Economic factors have a positive and significant effect on consumer decisions. H3 : Lifestyle has a positive and significant effect on consumer decisions.

RESEARCH DESIGN
This research was carried out at Gowa Regency's Royal Spring Housing. The chosen population is the home of the Royal Spring Housing PT Buana Sentosa Asri in Makassar. The number of samples calculated using the Slovin formulation is 82 household samples and is determined by simple random samples. The collection of data was carried out through the distribution of questionnaires to all respondents. This data analysis uses descriptive analyses, validity of tests and reliability tests, regression analyses to assess the impact of the independent variables on the dependent variable (family, economic conditions, lifestyle) (consumer purchasing decisions). Multiple equation of linear regression with three independent variables, namely: The t-test aims to partially assess whether family variables, economic circumstances and consumer affectly to purchasing lifestyle decisions. The criterion for decision-making is H0 if t-table>t-count at α = 5%. Ha is born when t count <t table or t count>t table α = 5%. The determinant is used to measure the influence of the studied independent variables (X1, X2, X3): family, economic and lifestyle variables on consumer buying decisions (Y), the dependent variable. The determinant coefficient (R2) ranges from zero to one (0 to R2 to 1, respectively). This means that R2 = 0 does not show the independent variable's influence on the dependent variable.

RESULT AND DISCUSSION RESULT
Data collection in this study was conducted through the distribution of questionnaires to respondents, namely consumers or homeowners in the Royal Spring Housing, through visits to the Royal Spring Housing and through the provision of questionnaires to homeowners who met and were willing to respond. The results of the validity test show that all question items with variables of family factors, economic factors, lifestyle factors and purchasing choices are valid and can be used as a tool for research measurements. This is demonstrated by the corrected item -Total > 0.217 value.
Reliability tests show how much a data collection tool can be trusted and used. The greater the device's reliability, the more reliable the measurement results are. The more trustworthy an instrument is, the agency obtains the same results if it measures the same object several times. The reliability method often used is Cronbach Alpha, which shows how consistent respondents respond to the instruments being evaluated. A research tool is said to be trustworthy if the value is > 0.60. The reliability of the research instrument is determined when the alpha of Cronbach is < 0.6, when reliability is considered to be low if the alpha of Cronbach is 0.6 -0.77, then reliability is considered to be sufficient, and when the Alpha of Cronbach > 0.8, the reliability is said to be good.  Table 1 shows that all variables used as instruments in the study are reliable and can be used as tools for data collection. Based on the results of the reliability test, it shows that the instrument has a very high degree of reliability; this is demonstrated by the alpha> 601 coefficient value so that measuring results can be trusted.
The normality test is carried out to see whether, in the regression model, the dependent variable and the independent variable both have a normal distribution or not. A good regression model is a regression model that is usually distributed. How to detect normality is done by looking at the graph histogram.

Figure 1. Histogram Graph
The histogram graph in Figure 1 shows that the histogram graph provides a distribution pattern near normal. This is shown by the fact that the map is symmetrical and follows a diagonal line. This histogram is not precise, however, particularly if the number of samples used is small. The standard probability plot is a reliable method. In the standard graph, the points around the diagonal line are dispersed, and the distribution follows the diagonal line direction.

Figure 2. Normal Prabability Plot
On the basis of Figure 2, the expected probability plot is shown that the point spreads around the diagonal line and the distribution is following the diagonal line, so the distribution pattern can be said to be typical. Looking at the two graphs above, the regression model can be concluded that in this study it meets the assumption of normality. Heteroscedasticity indicates that the variance of the variables is not the same for all observations. If the residual variance from one word to another is constant, it is called homoscedasticity. A good regression model is homoscedastic or does not occur Heteroscedasticity. Because the cross-section data have data representing various sizes (small, medium, and large). To detect Heteroscedasticity, the method used is the chart method (diagram Scatterplot). If there is a specific pattern registered with dots, which form a particular regular way (wavy, widened, then narrowed), Heteroscedasticity occurs. Figure 3 shows that the data is randomly dispersed and does not form a particular pattern; this indicates that no heterocedasticity is present. Therefore, it can be concluded that the variance of the residuals from one observation to another is different.

Figure 3. Scatterplot diagram
Multicollinearity test aimed at testing the correlation between the independent variable (independent) in the regression model. In a good regression model, there should be no correlation between variables. To test the presence or absence of multicollinearity in the regression model, it can be seen from the value tolerance and its counterpart, namely by looking at the variance inflation factor (VIF). A commonly used cut-off value is a value of patience 0.01. One way to test for multicollinearity can be seen from the Variance Inflation Factor (VIF). If the VIF value> 10, then multicollinearity occurs Based on table 2, the regression model for the independent variables proposed by the researcher is free of multicolinearity. The table 3, showing the VIF value of each independent variable <10, can prove this by determining its impact on buying decisions. The functional relationship between several independent variables with dependent variable bound was determined by a multiple linear regression test. The regression equation for the results of the analysis with the help of SPSS is: Y = 0,576 + 0,305X1 + 0,214X2 + 0,313X3 A constant of 0.576 indicates that if there are no changes in the variables of family factors, economic factors, and lifestyle factors, then the purchase decision is 0.576. The regression coefficient value for the family factor variable is 0.305. In this study, it can be stated that the family factor variable (X1) has a positive effect on purchasing decisions (Y), this indicates that when the family factor increases by one unit, the purchase decision will increase by 0.305. The regression coefficient value for the economic situation factor variable is 0.214. In this study, it can be stated that the economic situation factor variable (X1) has a positive effect on purchasing decisions (Y); this indicates that when the economic situation factor increases by units, the purchase decision will increase by 0.214. The regression coefficient value for lifestyle factor variables is 0.313. In this study, it can be stated that the lifestyle factor variable (X1) has a positive effect on purchasing decisions (Y); this indicates that when lifestyle factors increase by units, the purchase decision will increase by 0.313.
Simultaneous significance test or F-test aims to see the effect of the independent variables as a whole on the dependent variable. The test was carried out with a significance level of 0.05. If Fcount <F-table, then H0 is accepted, and Ha is rejected. Conversely, if F-count> F-table, then H0 is rejected and Ha is accepted.  Table 5 shows that the F-count value is 48.530, with a significance level of 0.000. The probability value (0.000) is smaller than 0.05, so the regression model can predict purchasing decisions. The F-count results compared with the F-table at the 95% confidence level (α = 0.05). It is known that the resulting F-count is 48.530, more significant than the F-table at the significance of α = 0.05, which is 2.72. The F test results show that the value of F-count> than F-table, which means that simultaneously independent variables included in the study, namely family factors, economic situation factors, and lifestyle factors, affect the dependent variable.
The coefficient of determination analysis is used to determine the percentage of the independent variables' influence simultaneously on the independent variable.

Discussion
This study states that accepting the first hypothesis proposed, namely family, positively affects consumers in making house decisions at the Royal Spring Housing PT. Buana Sentosa Asri in Makassar. These results indicate that the family's suggestions and information are the primary references for choosing a product, especially for products that tend to create conflicts in the family, meaning that they are of interest to all family members. This study's results support the results of research conducted by (Sari 2013;Towoliu & Tumbuan, 2017;Haryono, 2017), which show that family factors have a positive and significant effect on purchasing decisions.. This study states that accepting the second hypothesis proposed, namely, economic factors become the determining variable in purchasing decisions. These results indicate that economic factors positively affect consumers in making house decisions at the Royal Spring Housing PT. Buana Sentosa Asri in Makassar. When a consumer wants to make a purchase, he adjusts his financial condition to what he will buy. The greater the consumer economy's influence, the more excellent the opportunity for consumers to make purchases; on the contrary, the smaller the consumer's economy, the less chance for consumers to make purchases. This study's results support the research results (Hasanuddin 2016;Sunarto, 2018), which found that economic factors have a positive and significant effect on purchasing decisions.
This study states that accepting the second hypothesis proposed, namely Lifestyle factors, has a positive effect on consumers in making house decisions at the Royal Spring Housing PT. Buana Sentosa Asri in Makassar. The results showed that lifestyle had a positive and significant effect on purchasing decisions. This means that every change in buying decisions is influenced by lifestyle (Solihin, Tewal, & Wenas, 2020). That is, lifestyle reflects the whole person who interacts with the environment. People tend to choose specific products, services, or activities because they are associated with individual lifestyles. Lifestyle is a frame of reference that a person uses in behavior, and the consequences will form specific patterns of behavior (Ardiati, Hudayah, & Kuleh, 2017;Lomboan, Tampi, & Mukuan, 2020).

CONCLUSION
The study revealed that family factors, economic conditions and lifestyle have a positive effect on consumers in their decision-making at the Royal Spring Housing PT Buana Sentosa Asri in Makassar. As a property company, the results of this study suggest that PT Buana Sentosa Asri Land always follows current trends which are also adapted to the current lifestyle, especially in terms of mode and the type of house. The community's economic situation also influences the consumer's decision to buy a house to make the price of every product offered to consumers acceptable.