The dependent variable price of houses is determined by independent variables square footage of the house, number of bedrooms, number of bathrooms, the age of the house, and the area that the apartments are located. The most important independent variable in this relationship is the community's income because buying the homes in the market does not happen when there is no income coming into the relationship. Investors or individuals with many sources of income can easily afford houses in any residential area despite the prices.
Multiple independent variables determine the dependent variable of the price of houses. The change in one variable affects the difference in the considered variable; the variable is dependent. For example, if the number of rooms of the house increases or reduces, house prices will proportionately change. According to Savva (2018), a n increase in independent variables like footage of the house, number of bedrooms, number of bathrooms, and the house's age will lead to corresponding increase house prices.
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The primary independent variable is the income of the community because this ultimately affects the housing prices. After all, if the revenue in the entire town is a low-income city, compared to other cities with similar jobs and population, then the housing pricing would be within reason ( Savva, 2018) . Other variables also affect the price of houses, but they mostly affect individual homes and not the entire market.
The first independent variable is about the area the house is located within the city limits. This variable affects the house price because if the house is in a bad neighborhood within the city limits, no one would want to live in a high crime area that already has a high crime rate within city limits ( Zainon et al., 2017) . Besides, when a house is located in remote or far away for a city, the cost will be relatively low compared to when a home is built near cities.
The second independent variable is square footage for each property. This affects the housing prices because the more house you have, the more you have to pay, especially if the number of bedrooms is reasonable and within the average of bedrooms throughout the market. The square footage directly affects the size of the bedroom. For example, in case the size of the square footage is extensive, and they are used in building a house are many, the house prices will rise ( Zainon et al., 2017) . Conversely, if the sizes of square footage used to build houses are small and few, the sizes room will be smaller. Consequently, this will reduce house prices in both city areas and suburban areas.
The third independent variable is the age of the property. This happens because within the area, houses were built around 1900 so, homes usually are old in that area. The age of the house has significant effects on determining house prices. Old houses that make, for example, in the 1800s, can be less expensive than modern apartments built in the 21 st century using advanced technologies ( Zainon et al., 2017) . New houses tend to be more costly than old homes due to the technology used in the construction process. For example, the construction of new houses uses advanced modern technology. Therefore, the technology used in building homes directly determines house prices in both urban and suburban areas.
The number of bathrooms in a house is another essential independent variable that directly affects house prices, mostly in urban areas where class residents live. For middle and high-income earners in a country, the number of bathrooms and washrooms in houses is a key factor that directly affects price houses in urban areas ( Mohamad, Nawawi & Sipan, 2016) . The sizes of bathrooms and washrooms are also vital determinants that determine house prices, mostly in urban areas. Houses with small-sized toilets and bathrooms tend to be less expensive than apartments with considerably large bathrooms or washrooms.
The location of houses is another independent variable that directly affects house prices. Homes built in urban areas are more expensive than houses built in rural areas. In rural areas, the number of residents is many compared to the number of residents in remote areas ( Mohamad, Nawawi & Sipan, 2016) . As such, the demand for houses is high; thus, leading to an increase in house prices in urban areas compared to house prices in remote and rural areas.
Relationship between Independent and dependent Variables
There is a direct relationship between the primary independent variable and the dependent variable. Typically, community members' income level is a determinant demand and prices of houses in both rural and urban areas. When the level of income of residents in a city is high, the demand will increase. Successively, the prices of houses will increase. As such, the former implies that the primary independent directly affects the dependent variable.
Data Description
Determinants of Income Levels
| Total | Males | Females | |||||
| Variables | Group | N | % | n | % | n | % |
| Gender | Males |
1527 |
49.5 |
||||
| Females |
1558 |
50.5 |
|||||
| Age (years) | 40–49 |
1037 |
33.6 |
504 |
33 |
533 |
34.2 |
| 50–59 |
1018 |
33 |
490 |
32.1 |
528 |
33.9 |
|
| 60–69 |
1030 |
33.4 |
533 |
34.9 |
497 |
31.9 |
|
| Number of persons in the household | ≥ 2 |
2742 |
91.2 |
1325 |
89.5 |
1417 |
92.7 |
| 1 person | 266 | 8.8 | 155 |
10.5 |
111 | 7.3 | |
| Employment status | Full time |
1083 |
44.5 |
845 |
62.8 |
238 |
21.8 |
| Others |
1353 |
55.5 |
501 |
37.2 |
852 |
78.2 |
|
| Disposable income per household (JPY) | < 2000 K | 287 | 9.3 | 129 | 8.4 | 158 |
10.1 |
| 2000 K–< 6000 K |
1479 |
47.9 |
746 |
48.9 |
733 |
47 |
|
| ≥ 6000 K |
1319 |
42.8 |
652 |
42.7 |
667 |
42.8 |
|
| Disposable income of the respondent (JPY) | < 2000 K | 880 |
37.1 |
178 |
13.6 |
702 |
66.2 |
| 2000 K–< 5000 K | 805 |
33.9 |
515 |
39.2 |
290 |
27.3 |
|
| ≥ 5000 K | 689 |
29 |
620 |
47.2 |
69 |
6.5 | |
| Sleep duration on weekdays (hours) | ≥ 7 |
1241 |
40.4 |
656 |
43.2 |
585 |
37.7 |
| 6–7 |
1166 |
38 |
563 |
37.1 |
603 |
38.9 |
|
| < 6 | 663 |
21.6 |
299 |
19.7 |
364 |
23.5 |
|
| Physical exercise (days/week) | ≥ 3 | 429 |
14 |
225 |
14.9 |
204 |
13.2 |
| ≤ 2 | 530 |
17.3 |
293 |
19.4 |
237 |
15.3 |
|
| No exercise |
2097 |
68.6 |
994 |
65.7 |
1103 |
71.4 |
|
| Smoking | Never |
1605 |
52.2 |
449 |
29.5 |
1156 |
74.5 |
| Quit | 836 |
27.2 |
610 |
40.1 |
226 |
14.6 |
|
| Sometimes/everyday | 633 |
20.6 |
463 |
30.4 |
170 |
11 |
|
| Drinking (times/week) | Never |
1168 |
38 |
391 |
25.7 |
777 |
50 |
| ≤ 2 | 898 |
29.2 |
401 |
26.4 |
497 |
32 |
|
| ≥ 3 |
1008 |
32.8 |
729 |
47.9 |
279 |
18 |
|
| GHQ-12 score | ≥ 4 (poor) |
1131 |
36.9 |
506 |
33.4 |
625 |
40.4 |
| ≤ 3 |
1933 |
63.1 |
1010 |
66.6 |
923 |
59.6 |
|
Individual Income Data
| Table H-11. Size of Household - All Races, by Median and Mean Income: 30 observation | ||||||
| (Households as of March of the following year. Income in current and 2018 CPI-U-RS adjusted dollars(28)) | ||||||
| All Households | ||||||
Size of Household and year |
Number (thousands) |
Median income |
Mean income |
Average Household Size |
||
|
Current dollars |
2018 dollars |
Current dollars |
2018 dollars |
|||
| 2018 |
128,579 |
63,179 |
63,179 |
90,021 |
90,021 |
2.52 |
| 2017 (40) |
127,669 |
61,136 |
62,626 |
87,643 |
89,779 |
2.53 |
| 2017 |
127,586 |
61,372 |
62,868 |
86,220 |
88,322 |
2.53 |
| 2016 |
126,224 |
59,039 |
61,779 |
83,143 |
87,001 |
2.54 |
| 2015 |
125,819 |
56,516 |
59,901 |
79,263 |
84,011 |
2.53 |
| 2014 |
124,587 |
53,657 |
56,969 |
75,738 |
80,413 |
2.54 |
| 2013 (39) |
123,931 |
53,585 |
57,856 |
75,195 |
81,189 |
2.53 |
| 2013 (38) |
122,952 |
51,939 |
56,079 |
72,641 |
78,431 |
2.55 |
| 2012 |
122,459 |
51,017 |
55,900 |
71,274 |
78,095 |
2.54 |
| 2011 |
121,084 |
50,054 |
56,006 |
69,677 |
77,962 |
2.55 |
| 2010 (37) |
119,927 |
49,276 |
56,873 |
67,392 |
77,783 |
2.56 |
| 2009 (36) |
117,538 |
49,777 |
58,400 |
67,976 |
79,751 |
2.59 |
| 2008 |
117,181 |
50,303 |
58,811 |
68,424 |
79,997 |
2.57 |
| 2007 |
116,783 |
50,233 |
60,985 |
67,609 |
82,081 |
2.56 |
| 2006 |
116,011 |
48,201 |
60,178 |
66,570 |
83,111 |
2.56 |
| 2005 |
114,384 |
46,326 |
59,712 |
63,344 |
81,647 |
2.57 |
| 2004 (35) |
113,343 |
44,334 |
59,080 |
60,466 |
80,578 |
2.57 |
| 2003 |
112,000 |
43,318 |
59,286 |
59,067 |
80,840 |
2.57 |
| 2002 |
111,278 |
42,409 |
59,360 |
57,852 |
80,975 |
2.57 |
| 2001 |
109,297 |
42,228 |
60,038 |
58,208 |
82,758 |
2.58 |
| 2000 (30) |
108,209 |
41,990 |
61,399 |
57,135 |
83,545 |
2.58 |
| 1999 (29) |
106,434 |
40,696 |
61,526 |
54,737 |
82,754 |
2.60 |
| 1998 |
103,874 |
38,885 |
60,040 |
51,855 |
80,067 |
2.61 |
| 1997 |
102,528 |
37,005 |
57,911 |
49,692 |
77,766 |
2.62 |
| 1996 |
101,018 |
35,492 |
56,744 |
47,123 |
75,340 |
2.64 |
| 1995 (25) |
99,627 |
34,076 |
55,931 |
44,938 |
73,760 |
2.65 |
| 1994 (24) |
98,990 |
32,264 |
54,233 |
43,133 |
72,503 |
2.65 |
| 1993 (23) |
97,107 |
31,241 |
53,610 |
41,428 |
71,091 |
2.67 |
| 1992 (22) |
96,426 |
30,636 |
53,897 |
38,840 |
68,330 |
2.66 |
| 1991 |
95,669 |
30,126 |
54,318 |
37,922 |
68,374 |
2.62 |
Housing Prices Trends
|
Serial No. |
Date |
House prices (Median Sales Price of Houses Sold for the United States, Dollars, Quarterly, Not Seasonally Adjusted) |
|
1 |
2020-01-01 |
327100 |
|
2 |
2019-10-01 |
327100 |
|
3 |
2019-07-01 |
318400 |
|
4 |
2019-04-01 |
322500 |
|
5 |
2019-01-01 |
313000 |
|
6 |
2018-10-01 |
322800 |
|
7 |
2018-07-01 |
330900 |
|
8 |
2018-04-01 |
315600 |
|
9 |
2018-01-01 |
331800 |
|
10 |
2017-10-01 |
337900 |
|
11 |
2017-07-01 |
320500 |
|
12 |
2017-04-01 |
318200 |
|
13 |
2017-01-01 |
313100 |
|
14 |
2016-10-01 |
310900 |
|
15 |
2016-07-01 |
303800 |
|
16 |
2016-04-01 |
306000 |
|
17 |
2016-01-01 |
299800 |
|
18 |
2015-10-01 |
302500 |
|
19 |
2015-07-01 |
295800 |
|
20 |
2015-04-01 |
289100 |
|
21 |
2015-01-01 |
289200 |
|
22 |
2014-10-01 |
298900 |
|
23 |
2014-07-01 |
281000 |
|
24 |
2014-04-01 |
288000 |
|
25 |
2014-01-01 |
275200 |
|
26 |
2013-10-01 |
273600 |
|
27 |
2013-07-01 |
264800 |
|
28 |
2013-04-01 |
268100 |
|
29 |
2013-01-01 |
258400 |
|
30 |
2012-10-01 |
251700 |
Sources of Data
The above was obtained from different reliable sources which provide accurate and reliable time-series data or cross-sectional data set. Housing prices trend data was obtained from the Federal Reserve Economic Data of 30 observations made on house prices trends over the last 30 years. According to the data set, house prices, which are the dependent variable in this study has been changing over the past 30 years. The general increase in house prices over is associated with changing in one or more of the independent variables, which directly the dependent variable.
Another set of data is the US individual incomes over the last 30 years. Trend data were obtained from the Federal Reserve Economic Data of 30 observations made on house prices trends over the previous 30 years. The data set contains the mean, median, and average household, which are the key factors used to determine the size and house prices that family members live. The time series of data trend illustrates a general increase in income level over the past 30 years. The increase in individual income proportionately leads to high demand for houses hence has led to the rise in house prices in the United States.
References
Latif, N. S. A., Majeed, K. M. R., Rozzani, N., & Saleh, S. K. (2020). Factors Affecting Housing Prices in Malaysia: A Literature Review. International Journal of Asian Social Science , 10 (1), 63-68.
Mohamad, M. H., b Nawawi, A. H., & b Sipan, I. (2016). Review of building, locational, neighborhood qualities affecting house prices in Malaysia. Procedia-Social and Behavioral Sciences , 234 , 452-460.
Savva, C. S. (2018). Factors Affecting Housing Prices: International Evidence. Cyprus Economic Policy Review , 12 (2), 87-96.
Zainon, N., Mohd-Rahim, F. A., Sulaiman, S., Abd-Karim, S. B., & Hamzah, A. (2017). Factors affecting the demand for affordable housing among the middle-income groups in Klang Valley Malaysia. Journal of Design and Built Environment , 1-10.
Zhang, Z., Chen, R. J., Han, L. D., & Yang, L. (2017). Key factors affecting the price of Airbnb listings: A geographically weighted approach. Sustainability , 9 (9), 1635.
