North Valley Real Estate Analysis

North Valley Real Estate Analysis


A data-driven atmosphere characterizes the contemporary commercial marketplace. There are many things that businesses can do with their business data, and statistical analysis is a smart way for them to investigate and learn from it. It covers a variety of data components, such as surveys, data collection, and experiments. The purpose of data is to provide the leadership of an organization with the ability to base decisions on facts, trends, and statistical statistics (Black, 2019). However, since there is much information available, the leaders of businesses need to have the ability to filter through the irrelevant data and find the relevant data to be able to make the best judgments possible about development strategy. If a company is not paying much attention to its data, it misses out on a world of opportunities. With the assistance of business analytics, a business can achieve more with its data than before; the management needs to know where to begin (Black, 2019). Using statistical analysis, companies make better decisions and significantly increase their profitability.


The use of business analytics in the running of businesses can be seen across all sectors of the economy, and the real estate sector is not an exception. Data is the driving force behind practically all the decisions made in the real estate market. Directors of human resources collect information from various websites to find the most qualified candidates for open positions and validate specifics about those candidates (Munawar et al., 2020). Marketing departments focus on market segmentation data to discover potential customers, and executives focus on more significant movements in the market, such as shifts in the prices of commodities.

In a world saturated with commercials and material, statistical analysis may help deliver a company’s message to its target audience and analyze its effectiveness. It aids in revealing consumer tendencies, patterns, connections, and preferences. The key to effectively using business data is not how much a firm possesses but how it uses it. A real estate company may examine data from any source and format to find answers to most of their difficulties. Before business statistics, many real estate choices depended primarily on experience and limited trend research (Munawar et al., 2020). Real estate professionals utilize real-time, comprehensive data from various sources to make better-educated judgments. Real estate service providers offering customized, customer-centric property solutions increase customer satisfaction (National Association of Realtors, 2021). Insights from data also help suit the demands of buyers and sellers. Thus, service providers might present themselves as potential clients’ preferred real estate partners.

Today, searching for a house to purchase or rent is an internet activity conducted through websites, apps, and other online platforms. Research conducted by the U.S. National Association of Realtors (2021) states that 97% of house purchasers utilized the internet to look for properties, while 51% located the home they ultimately bought online. Pre-qualifying for a mortgage, virtual walk-throughs, seeing the exterior of a property or neighborhood, and obtaining further information were the most frequent activities completed as a result of such Internet searches (National Association of Realtors, 2021). Real estate agents, homebuyers, investors, and financial companies may now access data with a single click. This enables them to make wiser judgments since data analysis enables accurate risk and market trends forecasts.


The paper will determine if the statistical analysis impacts a real estate company’s success, health, and viability. Thcriticalportant dependent variable in this relationship is the performance of a real estate company measured mainly by the amount of yearly revenue that a company makes. The use of statistical analysis will be our independent variable.

Purpose Statement

The North Valley Real Estate company in the United States wants to evaluate the correlation between several data elements. The multiple parameters include the house size, number of rooms, the house’s price, bath size, and the presence of a garage. The dependent factor chosen is the house value since the dimensions of the property determine it, the number of bedrooms, the dimensions of the bath, and the presence of a garage (Benefield et al., 2019). The most crucial variable in the independent domain is the magnitude of the property since it points out and conveys the best coefficient of correlation: 0.952. The correlation coefficient is significant, with a p-value of 0.000. This can also be viewed as evidence of a stronger linear relationship.” According to Seber and Lee (2012), in their research topic on “Linear Regression Analysis,” regression analysis helps detect and describe associations for various constituents in a  study. The multiple components in this research represent the different parameters for the investigation. The parameters comprise the home’s value, dimensions, and bedroom number. Akoglu (2018) argue that “Correlation Coefficients” terms correlation as measuring the link between parameters. It might be dependent against independent or independent versus dependent.

The equation for the prototypical is (Revesz, 2022):

where by is the response variable.

is the constant value. It is the cost of the house when all the other factors, that is, the independent variables, are zero.

is the property dimensions coefficient. It is an amount multiplied by the property’s size to provide the precise amount of the response factor.

denotes a variety of distinct house sizes. They are expressed in sq ft. In this study, household size is a predictor variable. It is multiplied by its coefficient.

is a coefficient that is multiplied by the number of bedrooms.

is the second variable in determining the response factor. The quantity from the number of bedrooms is obtained by multiplying the parameter by its coefficient factor.

is a coefficient multiplied by the sq. ft. magnitude of the household bath.

is the factor determining the bath size. When the variable is multiplied by its coefficient, it gives the dimension of the house bath.

is the coefficient of the house garage when available in an apartment.

denotes the presence of the garage in the apartment. It is a yes or no question. A value of 1 represents a yes, and 0 is used to express a no.


Definition of Variables

The U.S. Company must determine the relationship between house sizes, number of bedrooms, bath size, and presence of a garage. The dependent variable in the study will be housing prices. It is a response factor since it is projected by the study’s explanatory parameters (Kumari & Yadav, 2018). The parameter is essentially initiated by adding the other predictor variables. It is represented by on, the other side of the regression model in this study. The parameter is measured in dollars because it is what individuals pay for houses in the United States (Mathur, 2020).

In the linear model, the sizes of the apartments are represented by . It is the model’s initial independent variable, and its multiplication with its coefficient aids in determining the explanatory variables. The parameter is measured in square feet since it represents the size of the house. As the function is multiplied by its coefficient and added to the other variables, it determines  (the criterion variable). The variable’s coefficient is expected to have a positive sign because it increases the value of the quantity in dollars of the response variable.

Another predictor variable denoted in the data source with the symbol  in the regression model is the number of bedrooms. It is quantified in numbers and helps determine  by multiplying by its coefficient and adding to the other variables in the equation. The variable is likewise predicted to be positive because an increase in number of rooms leads to an increase in the result, which is the value of the dependent variable.

The bath size is another variable denoted by  in the linear regression model. The variable is measured in square feet as it represents the dimension of the bath. It aids in determining the price of the house when multiplied by its coefficient and added to the rest of the factors. Manasa et al. (2020) argue that buildings without baths can prove more challenging for real estate agents when marketing effectively. Similarly, a house with a small bath can take a long before it is sold, and a home with a large dimension may attract numerous potential buyers or customers. The variable’s value must be positive since it results from measurements.

The presence of a garage is another crucial element that affects property sales and is denoted by . According to recent studies, a garage can add up to 5% to the cost of a typical home (Brown et al., 2020). Garages and parking spots are more in demand than ever, so having – or adding – one attached to your house can dramatically improve its value if you decide to sell. In the data collection process, homeowners were asked to answer a yes or no question as to whether they had a garage or a parking lot around their houses. A value of 1 was used to represent a yes, and 0 denoted a no response. The variable is multiplied by its coefficient, and when added with the rest of the independent variable, it determines the price of the house.

Data Description

The data employed in this study comprise numerous characteristics for 30 houses from North Valley. These characteristics include the size of the residences in square feet, the number of bedrooms, bath size, and the presence of a garage. The sample data for the dwellings are shown below. The data was taken from the North Valley real estate dataset records. The information was gathered from the United States Census data on housing. The data has no constraints because there was just one source, census data, and it was also complete, which means that all variables had values.


Price Size Bedrooms Baths Garage (Yes is 1)
206424 1820 2 1.5 1
346150 3010 3 2 0
372360 3210 4 3 1
310622 3330 3 2.5 0
496100 4510 6 4.5 1
294086 3440 4 3 1
228810 2630 4 2.5 1
384420 4470 5 3.5 1
416120 4040 5 3.5 1
487494 4380 6 4 1
448800 5280 6 4 1
388960 4420 4 3 1
335610 2970 3 2.5 1
276000 2300 2 1.5 0
346421 2970 4 3 1
453913 3660 6 4 1
376146 3290 5 3.5 1
694430 5900 5 3.5 1
251269 2050 3 2 1
547596 4920 6 4.5 1
214910 1950 2 1.5 0
188799 1950 2 1.5 0
459950 4680 4 3 1
264160 2540 3 2.5 1
393557 3180 4 3 1
478675 4660 5 3.5 1
384020 4220 5 3.5 1
313200 3600 4 3 1
274482 2990 3 2 0
167962 1920 2 1.5 1



Summary output


Model Summary
Model R R Square Adjusted R Square Std. The error in the Estimate Change Statistics Durbin-Watson
R Square Change F Change df1 df2 Sig. F Change
1 .922a .849 .825 49281.198 .849 35.198 4 25 .000 1.207
a. Predictors: (Constant), Garage (Yes is 1), Size, Bedrooms, Baths
b. Dependent Variable: Price



Residuals Statistics
  Minimum Maximum Mean Std. Deviation N
Predicted Value 183860.81 568073.31 360048.20 108585.667 30
Residual -82900.461 126356.695 .000 45756.444 30
Std. Predicted Value -1.623 1.916 .000 1.000 30
Std. Residual -1.682 2.564 .000 .928 30
a. Dependent Variable: Price



Model Sum of Squares df Mean Square F Sig.
1 Regression 341934567764.144 4 85483641941.036 35.198 .000b
Residual 60715912576.656 25 2428636503.066    
Total 402650480340.800 29      
a. Dependent Variable: Price
b. Predictors: (Constant), Garage (Yes is 1), Size, Bedrooms, Baths


Model Unstandardized Coefficients Standardized Coefficients t Sig. 95.0% Confidence Interval for B Correlations
B Std. Error Beta Lower Bound Upper Bound Zero-order Partial Part
1 (Constant) 6173.676 32342.570   .191 .850 -60437.094 72784.446      
Size 79.630 15.779 .740 5.047 .000 47.133 112.127 .915 .710 .392
Bedrooms -7330.649 34269.648 -.083 -.214 .832 -77910.311 63249.012 .811 -.043 -.017
Baths 40656.895 53583.937 .313 .759 .455 -69701.289 151015.078 .826 .150 .059
Garage (Yes is 1) -13563.616 28368.927 -.047 -.478 .637 -71990.516 44863.284 .395 -.095 -.037
a. Dependent Variable: Price


The regression equation for the research study is:



Because it depicts the home’s overall size, the size parameter is measured in square feet. The house price is calculated by multiplying the function by its coefficient and then adding it to the results of the other variables. The number of bedrooms can be expressed as a number and contributes to the overall cost of the home by being multiplied by a coefficient and added to the effects of the other variables in the equation. It is also anticipated that the variable’s value will be positive because a rise in the number of rooms will lead to an increase in the outcome, which is the variable’s value that is dependent on the number of rooms. Because it represents the dimension of the bath, the size of the bath is measured in square feet. When multiplied by the factor’s coefficient and added to the other considerations, it contributes to establishing the house’s price. Last but not least, the existence of a garage is yet another essential factor that plays a role in home sales. If you ever decide to sell your home, it will be significantly more valuable if it already has an attached garage or if you build one onto it. Garages and parking spots are in higher demand than they have ever been.


This is the most accurate method for determining the value of a home. The size, number, and sizes of bedrooms and bathrooms, as well as the presence of a garage, should all impact the house’s value or price. This is the best method for real estate agents to use when calculating house prices. This not only benefits the sellers but can also assist buyers in estimating the value of the home they are purchasing.


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