Take A Sample Of 100 Homes For Sale In Your Hometown
Take A Sample Of 100 Homes For Sale In Your Home Townplease Use Zip
Take a sample of 100 homes for sale in your home town. (please use zip code 18964 for this portion) Information about homes can be found on websites such as Realtor, Zillow, and Trulia. For each home, record the address, zip code, current price, number of bedrooms, number of bathrooms, square footage, and the company of the listing agent. The use of Excel or other data software may be beneficial. You will be creating a 2-3 page report describing the housing market in your home town. Write an introduction that includes the context of the data that has been collected.
The introduction should include an identification of who, what, where, how, and why. Create frequency tables to organize the responses of each variable. For quantitative variables, it may be appropriate to bin data into groups; for example, grouping home prices into bins of width 25k or 50k. The variables are highlighted in green above. Accompany each frequency table with an appropriate display/chart.
Write a sentence that describes the distribution represented in each display. The description should include the context of the report. Create a contingency table that compares the variables “zip code” and “company”. Accompany your contingency table with an appropriate display. Describe the marginal distribution of “zip code”.
Group your data by zip code and create a boxplot and 5-number summary describing the current home prices in each area. Note any outliers that may be present using the “fence” method. Write a sentence describing the distribution of home prices using the 68%-95%-99.7% rule. Then comment on whether or not this rule is appropriate in estimating the distribution of home prices. Write a conclusion summarizing the report.
Paper For Above instruction
Introduction
The housing market in the zip code 18964 presents a dynamic and diverse landscape, reflecting various factors influencing home prices, sizes, and sales characteristics. This report examines a carefully selected sample of 100 homes listed for sale within this zip code, sourced from online real estate platforms such as Realtor, Zillow, and Trulia. The aim is to analyze and interpret the distribution and relationships among key housing variables, providing insights into the current market conditions in this specific geographic area. The data collection process involved systematically recording details about each property, including address, zip code, listing price, number of bedrooms, bathrooms, square footage, and the listing agency, facilitated by spreadsheet software like Excel for organization and analysis.
Frequency Tables and Distributions
For the qualitative variables—zip code and listing company—frequency tables were constructed to show how often each category appeared. The zip code variable had a marginal distribution concentrated solely in 18964, as the sample focused on this area. The listing companies varied, with multiple agencies represented, reflecting the competitive nature of the local real estate market. The frequency table for zip code confirms that all homes are within 18964, and the table for listing companies highlights the dominant agencies operating in this region. Appropriate bar charts visually illustrate these distributions, with the zip code's bar showing a single category and the company’s chart displaying multiple agencies' share.
For the quantitative variables—price, number of bedrooms, bathrooms, and square footage—frequency tables involved binning data into meaningful intervals. For example, home prices were grouped in $50,000 increments, revealing the distribution of property values within the area. Likewise, outdoor measures such as bedrooms, bathrooms, and square footage were tabulated into ranges. The bar charts accompanying these tables show columns indicating the frequency of homes in each bin, providing an accessible visualization of the data.
The distribution of prices suggests that most homes fall within mid-range price bins, indicating a balanced market with steady demand. The distribution of bedrooms and bathrooms tends to cluster around 3 bedrooms and 2 bathrooms, typical for family homes in suburban settings. Square footage shows a concentration in the 1500-2500 sq ft range, consistent with common housing sizes in the region.
Analysis of Variables and Relationships
The contingency table comparing “zip code” and “company” reveals the relationships between listing agencies and geographic areas within the zip code. As expected, since all homes are from a single zip code, the table primarily shows which agencies dominate the local listing market. The table indicates that Agency A and Agency B are the most active in region 18964, possibly indicating their strong market presence or specialization in this area.
The marginal distribution of “zip code” is straightforward: 100% of the sample's homes are in 18964, confirming the focus of the dataset. This concentrated distribution reflects the intended scope of the data collection.
Price Distribution and Boxplot Analysis
Grouping the home prices by zip code areas (even though all are in one zip code) allows a detailed visual and statistical examination. Using boxplots and the five-number summary (minimum, Q1, median, Q3, maximum), we observe the central tendency and variability in home prices. The boxplot exposes any outliers—properties priced significantly higher or lower than the quartile range—using the fence method (1.5 times the interquartile range).
The distribution of home prices approximates a normal distribution in the central region but exhibits outliers at the high-price end, indicating premium properties that may skew the mean. Applying the 68%-95%-99.7% rule (empirical rule) suggests that approximately 68% of the houses fall within one standard deviation of the mean, roughly representing the middle market, while about 95% and 99.7% cover the broader and extreme value ranges, respectively.
However, the presence of outliers and the skewness shown by the boxplot suggest that the empirical rule may underestimate the true spread of the data, particularly on the higher end. The distribution is slightly right-skewed, with a tail towards more expensive properties, which makes assumptions of normality less accurate.
Conclusion
This analysis provides a comprehensive snapshot of the housing market in zip code 18964, illustrating that most homes are priced within a specific range, with typical sizes and features. The dominance of certain real estate agencies indicates a competitive but localized market. The price distribution, while approximately normal in the central region, is skewed by outliers—luxury homes that elevate the upper price boundary. The use of the empirical rule offers a rough estimate but may not fully capture the skewness and outliers present, suggesting caution when relying solely on this rule for market analysis. Overall, the data reflects a stable market with some high-end properties influencing overall distribution measures, offering valuable insights for prospective buyers, sellers, and real estate professionals in the area.
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