The Chatham company is a company that mainly engages in real estate trading and may seek to look into new potential areas to expand. The expansion is in order to increase their profits, which is the main aim of each and every company. Real estate mainly alludes to land, air rights, buildings, property, and below the ground underground rights. The real market for real estate is divided into the market for space and assets(Wheaton et al., 1992).These markets achieve a different level of efficiency(Tirtiroglu et al., 1995). Chatam Company is mainly involved in trading in all types of real estate. These real estates include co-ops, townhouses, strip malls, educational buildings, manufacturing buildings, warehouses, and vacant land.
The production, buying, and sale of real estate are all generally referred to as a real estate.The construction of new houses is a big chunk of the GDP in the United States. These new buildings are commercial, residential, and industrial buildings. The construction of real estate translated to about 6.2% of the gross domestic product. The amount contributed by this sector was $1.15 trillion. Progress in this amount has been commendable from the previous figure of $1.13 trillion. However, in that period, the real estate was about 9% of the Gross Domestic Product(GDP).
With the help of homeowners, investors, and businesses engage in the sale and purchase of all the four categories of land. Each specialist focuses on one of the four types of real estate. Either through Multiple Listing Service or their professional contacts, seller’s agents assist in finding buyers of real estate. Similar services are provided by the buyer’s agent to the home purchaser. Buyer’s agents have a good understanding of the local market. This means to home purchasers’ they are of great assistance as they can get property fitting any description. A comparison of prices allows them to guide home purchases to areas that are affordable and also can negotiate prices on behalf of the purchaser.
After the real estate industry hit its highest peak in 2006, followed by a sudden decline in the following year. From 2008 the prices have shown a trend of increasing but a significantly lower rate compared to the period before 2006. This has forced companies to come up with new techniques to increase profits. One of the ways considered in increasing profits, especially of Chatam Company, is expanding into new neighborhoods and cities. Effective expansion plans need to be prepared in order to make wise decisions. These plans should be created backed by facts that are provided by data. The practice of data mining is important as it examines previous records of data in order to come up with new information.
In the pricing of houses, there are some factors that are considered, such as the availability o social amenities and their proximity, the neighborhood location, e.g., in prime beach plots, the prices of land are very high, and the age of the house on sale. Data mining techniques can be used to extract meaningful aspects of the data on real estates, such as those mentioned earlier that will be used to make informed decisions. Estimation of real estate prices is difficult because of the differences in quality(Bailey et al., 1963).
The data set contains various variables such as the date the sales transaction was carried out, the house age, the nearest MRT’s distance, the count of convenience stores, the location of the place, which is indicated by the longitude and latitude of the real estate. All these factors appear to have a significant impact on housing prices.
The table below shows the descriptive statistics for the data.
No X1 transaction date X2 house age
Min. : 1.0 Min. :1905-07-04 16:00:00 Min. : 0.000
1st Qu.:104.2 1st Qu.:1905-07-04 22:00:00 1st Qu.: 9.025
Median :207.5 Median :1905-07-05 04:00:00 Median :16.100
Mean :207.5 Mean :1905-07-05 03:34:30 Mean :17.713
3rd Qu.:310.8 3rd Qu.:1905-07-05 10:00:00 3rd Qu.:28.150
Max. :414.0 Max. :1905-07-05 14:00:00 Max. :43.800
X3 distance to the nearest MRT station X4 number of convenience stores
Min. : 23.38 Min. : 0.000
1st Qu.: 289.32 1st Qu.: 1.000
Median : 492.23 Median : 4.000
Mean :1083.89 Mean : 4.094
3rd Qu.:1454.28 3rd Qu.: 6.000
Max. :6488.02 Max. :10.000
Y house price of unit area
Min. : 7.60
1st Qu.: 27.70
Median : 38.45
Mean : 37.98
3rd Qu.: 46.60
Max. :117.50
The correlation table between the dependent variables in the data, which are: the nearest MRT’s distance, the count of convenience stores around,the house’s location, and the age of a house are the independent factors on which the house price is based.
| Factors | Correlation with the house price |
| Distance to the nearest MRT | -0.673961 |
| The number of convenience stores | 0.5710049 |
| The latitude. | 0.5232865 |
| The Longitude | 0.5232865 |
Coefficients:
(Intercept) d_mrts
45.851427 -0.007262
Price and distance to nearest MRT regression model
Analysis of Variance Table
Response: price
Df Sum Sq Mean Sq F value Pr(>F)
d_mrts 1 34695 34695 342.24 < 2.2e-16 ***
Residuals 412 41767 101
—
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Price and Number of Convivnient stores regression model
Coefficients:
(Intercept) n_con
27.181 2.638
Analysis of Variance Table
Response: price
Df Sum Sq Mean Sq F value Pr(>F)
n_con 1 24930 24930.0 199.32 < 2.2e-16 ***
Residuals 412 51531 125.1
—
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Price and latitude simple regression model
Coefficients:
(Intercept) ltd
-56345.6 463.9
Analysis of Variance Table
Response: price
Df Sum Sq Mean Sq F value Pr(>F)
ltd 1 20937 20937.3 155.36 < 2.2e-16 ***
Residuals 412 55524 134.8
—
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Price and latitude regression model
Coefficients:
(Intercept) lat
-14918 599
Analysis of Variance Table
Response: price
Df Sum Sq Mean Sq F value Pr(>F)
lat 1 22820 22820.0 175.27 < 2.2e-16 ***
Residuals 412 53641 130.2
—
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
The regression model between price and the age of a house
Coefficients:
(Intercept) age
42.4347 -0.2515
Analysis of Variance Table
Response: price
Df Sum Sq Mean Sq F value Pr(>F)
age 1 3390 3390.2 19.115 1.56e-05 ***
Residuals 412 73071 177.4
—
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Multiple Linear regression model between price and all the other six variable.
Coefficients:
(Intercept) d_mrts n_con ltd lat age
-4.946e+03 -4.259e-03 1.163e+00 -7.805e+00 2.378e+02 -2.689e-01
Analysis of Variance Table
Response: price
Df Sum Sq Mean Sq F value Pr(>F)
d_mrts 1 34695 34695 431.7053 < 2.2e-16 ***
n_con 1 3274 3274 40.7336 4.746e-10 ***
ltd 1 24 24 0.2924 0.589
lat 1 1858 1858 23.1205 2.144e-06 ***
age 1 3822 3822 47.5557 2.044e-11 ***
Residuals 408 32790 80
—
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Comparing the five regression models using ANOVA to determine which is the best.
Analysis of Variance Table
Model 1: price ~ n_con
Model 2: price ~ lat
Model 3: price ~ ltd
Model 4: price ~ age
Model 5: price ~ d_mrts + n_con + ltd + lat + age
Res.Df RSS Df Sum of Sq F Pr(>F)
1 412 51531
2 412 53641 0 -2110
3 412 55524 0 -1883
4 412 73071 0 -17547
5 408 32790 4 40282 125.31 < 2.2e-16 ***
—
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
The first row of the data was made static to indicate the labels in the data. The data was primarily arranged in a list. The data set individual variable arrays were unlisted to ease the analysis of the data. The format of the data, i.e., changing the type of characters in the array to numeric and the column with the date to date to indicate the characteristics of the data.
The real estate valuation data set is a multivariate set. However, the individual variables can be analyzed independently. However, multivariate methods such as multiple regression are better suited compared to univariate methods such as simple linear regression. Multiple regression allows for the investigation of six variables’ joint performance on the process that produces them, which is indicated by the prices of the houses.The determination of the effect that a variable has Is possible in multiple regression.
A strong positive correlation is observed between a house’s location and its price. The correlation is about 0.5233. This correlation is equal to that of price and latitude. This implies that the combination of data and latitude is arranged such that destinations can be pinpointed exactly. The two can be simply defined as the location description. The correlation between the house’s price and its location is statistically significant. The correlation between the count of convenience stores and a house’s priceis also very high at 0.57. A statistically significant relationship can be observed between price and the number of convenience stores around. The correlation between the distance to the nearest MRT is strong but negative. This implies as either the price or the MRT’s nearest distance increases, meaning that the other variable reduces. This shows an inverse relationship between the two.
Acquiring univariate models to compare relationships in the data, the model between the nearest MRT’s distance yields an equation y=-0.007262x+45.85. The model for the count of convenience stores and a house price is y=2.638x+27.181. The model between the price and location represented by the longitude and latitude y=-56345.6x+463.9. The model between the age of the house and the price can be represented by the equation y=-0.2515x+42.4347. It is possible to fit a linear regression model on all the variables x1,x2,x3,x4, and x5 representing the count of convenience stores around, the longitude, location, and the age of the real estate, respectively, the model is represented by the equation y=-0.00423×1+1.163×2-7.805×3+0.02378×4-0.02689×5.
The models were analyzed using ANOVA tables to determine which was the best method for use. Calculating the r squared factor for all the models based on the prices, the longitude has a value of 0.27383, the number of convenience stores has 0.32605, distance to the nearest MRT has 0.45375, house age has 0.04434 while the multiple regression model has a value of 0.57116. The highest r squared statistics is by the multiple regression model. The univariate models ranked by their r statistic follow the order; age, location, count of conveniences stores, and the nearest MRT’s distance. The higher the r square value, the better the model fits the data.
The model gives a chance to the company to expand its activities into a new area. To determine the criteria for selection of the real estates and also to estimate the pricing, the multiple linear regression equation contains the weights that can be used to guide decisions. The weights multiplied by the values of the six variables representing the houses’ age, the location, the nearest MRT’s distance, and the count of convenience stores around. The weights are 0.00423,1.163,-7.805,0.02378 and 0.02689 respectively for the variables.
The results should be evaluated according to the analysis of the competition in the given area, given the average prices of houses in the area. A high average in the mean price of the house indicates that the business in the area is lucrative. A business model should be developed around the weights that are given by the best model, multiple regression model. The model can be used to develop an informed plan on best real estate, implying sales pick early due to a good understanding of the factors affecting the industry. This model is perfect for the description of this scenario of pricing the houses, especially considering that in multivariate models, it is possible to explore the joint performance of the variables on the process that produces them. Multiple regression has been useful for the determination of a house’s appraisal(Reicher et al., 1991).
Implementation of a plan with the criteria of the weights gives information on the factors that are to be given priority during the selection of real estate. The model gives a suspicious r square at around 50%. This implies that another 50% are left to chance, which is risky. The architecture in this area is different from that in other areas. The company should avoid giving opinions on housing, such as opinions on building structures or renovations.There are more factors that affect housing, such as listing price and the real estate condition(Gardeber et al., 1989). Some of these have not been incorporated into the data. This is what makes real estate more volatile than other ventures. Running a pilot start-up to gauge the reaction of investors to their services for a short period of time should ensure that they have a better understanding of the factors affecting pricing.
REFERENCES
DiPasquale, D., & Wheaton, W. C. (1992). The markets for real estate assets and space: A conceptual framework. Real Estate Economics, 20(2), 181-198.
Gatzlaff, D., &Tirtiroğlu, D. (1995). Real estate market efficiency: issues and evidence. Journal of Real Estate Literature, 3(2), 157-189.
Kang, H., & Gardner, M. (1989). Selling price and marketing time in the residential real estate market. Journal of Real Estate Research, 4(1), 21-35.
Chau, K. W., MacGregor, B. D., & Schwann, G. M. (2001). Price discovery in the Hong Kong real estate market. Journal of Property Research, 18(3), 187-216.
Bailey, M. J., Muth, R. F., &Nourse, H. O. (1963). A regression method for real estate price index construction. Journal of the American Statistical Association, 58(304), 933-942.
Kang, H. B., & Reichert, A. K. (1991). An empirical analysis of hedonic regression and grid‐adjustment techniques in real estate appraisal. Real Estate Economics, 19(1), 70-91.
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