Median Housing Price Prediction Model for D.M. Pan Real Es ✓ Solved

The purpose of this report is to provide the sales team at D.M. Pan Real Estate Company with data that examines the relationship between the selling price of properties and their size in square feet. The data will be from the Mountain region of the US.

This analysis will focus on the examination of the median listing prices, median square feet, and the correlation between these two variables using regression analysis. The dataset will be represented through various county data, median listing prices, and respective square footage across multiple states in the Mountain region.

Representative Data Sample

The following table summarizes the median listing prices and median square footage for properties in the Mountain region:

  • Arizona: Cochise - $200,261, Gila - $360,235, Mohave - $304,891, Pima - $291,769, Yavapai - $457,534

  • Colorado: Adams - $418,735, Boulder - $620,894, Denver - $534,393, Eagle - $699,443, Garfield - $611,190, La Plata - $540,468, Weld - $417,120

  • Idaho: Bannock - $255,125, Canyon - $287,786, Twin Falls - $265,805

  • Montana: Flathead - $485,857, Lewis and Clark - $318,840, Yellowstone - $281,365

  • New Mexico: Dona Ana - $223,966, Lea - $186,791, Otero - $190,429, Sandoval - $304,791, Valencia - $231,239

  • Nevada: Clark - $319,031, Elko - $290,651, Washoe - $467,923

  • Utah: Davis - $398,894, Tooele - $336,615, Washington - $408,547

  • Wyoming: Laramie - $324,248

Data Analysis

The analysis began with a sample of 30 properties selected through systematic sampling from the Mountain region population. This sampling method involves choosing every second entry until reaching the desired sample size. The mean and median of the residential properties chosen for analysis was greater than the overall regional sample, suggesting higher property values in the sampled subset.

Key statistics observed are as follows:

  • Mean Median Listing Price: $367,828

  • Median Listing Price: $321

  • Mean Median Square Feet: 1,900 sq. ft.

  • Standard Deviation of Listing Prices: $134,739

Scatterplot and Regression Analysis

The analysis utilized scatterplots to visualize the relationship between median square feet and median listing prices, establishing the predictor variable and the response variable. The regression equation discovered from the data was formulated as follows: Y = 74.683X + 204027.

This regression allows predictions based on the size of the property. For instance, predicting the listing price for a 1,200 sq. ft. home would yield the following:

  • Y = 74.683(1200) + 204027 = $293,592.60

This projection supports the notion that property size directly influences the listing price within this region.

Furthermore, the R-squared value derived from this regression analysis serves as a critical metric, representing the proportion of variance in the median listing price accounted for by the model. A higher R-squared value indicates a stronger predictive capability of the given regression model.

Conclusions

This report delineates the relationship between square footage and listing prices effectively, signifying the importance of size in property valuations. Key considerations crucial for understanding this correlation include:

  • The comparative analysis between the selected Mountain region and overall US residential properties.

  • The impact of the slope in the regression equation as an indicator of price fluctuations.

  • The potential utility of the regression model in determining fair listing prices based on sized properties.

Future Recommendations

To enhance the accuracy of the model, additional factors could be incorporated, such as locality, property age, and additional amenities, which may significantly influence listing prices. The development of an interactive pricing tool or software for agents could leverage this modeling approach, aiding in precise pricing strategies. The continued updating of data sets will also ensure models remain relevant with market changes.

References

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