Real Estate Market Analysis: East North Central Region
This paper presents a comparative analysis of residential real estate markets across the East North Central region of the United States, covering Illinois, Indiana, Michigan, Ohio, and Wisconsin. Using a simple random sample of 30 county-level listings, the study examines listing prices, price per square foot, and square footage. Descriptive statistics — including mean, median, and standard deviation — are calculated for listing prices and square footage. The paper also explores how regional factors such as local economic conditions, housing stock composition, and demographic trends can cause regional markets to diverge from national patterns, and uses a scatterplot regression to demonstrate the positive relationship between square footage and listing price.
- Introduction: Scope and purpose of regional real estate analysis
- Representative Data Sample: Thirty county-level listings with price and size data
- Data Analysis and Descriptive Statistics: Mean, median, and standard deviation for key variables
- Regional Factors Affecting the Sample: Local economic and housing factors shaping regional differences
- Sampling Methodology: Simple random sampling process using Excel
- Scatterplot and Regression Analysis: Positive price-to-square-footage relationship and prediction
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What makes this paper effective
- Grounds statistical findings in concrete context by connecting descriptive statistics directly to regional economic and demographic factors that explain divergence from national trends.
- Transparently explains the sampling method, including the specific tool used (Excel random number generator), which strengthens methodological credibility.
- Applies a scatterplot and regression trendline to make the data actionable, illustrating how the analysis can support a real pricing decision for an 1,800 sq. ft. home.
Key academic technique demonstrated
The paper demonstrates descriptive statistical analysis applied to a real-world dataset. It calculates mean, median, and standard deviation for two continuous variables (listing price and square footage), then contextualizes those figures by acknowledging sampling limitations and confounding variables — a hallmark of careful quantitative reasoning.
Structure breakdown
The paper opens with an introduction establishing the economic importance of real estate markets and the regional scope. It presents the raw data sample in tabular form, followed by a dedicated statistics section reporting key measures of central tendency and spread. The analysis section addresses how regional factors can cause the sample to differ from national norms. A methodology section justifies the sampling approach, and the paper closes with a scatterplot interpretation that applies the regression equation to a practical prediction scenario.
Introduction
The real estate market is an essential component of the overall economy, impacting various sectors such as construction, finance, and consumer spending. Understanding regional real estate market dynamics can provide valuable insights for investors, homeowners, and policymakers. This report focuses on analyzing real estate data from the East North Central region of the United States, comprising the states of Illinois, Indiana, Michigan, Ohio, and Wisconsin.
Representative Data Sample
The table below presents the 30-observation random sample drawn from the East North Central region. Each row records the state, county, listing price, price per square foot, square footage, and the random number used during sampling.
Region: East North Central | Variables: State, County, Listing Price, $ per Square Foot, Square Feet, Random Number
Illinois – Vermilion County: $254,500 | $156/sq ft | 1,632 sq ft | random: 0.772534
Illinois – Coles County: $220,800 | $117/sq ft | 1,893 sq ft | random: 0.43187
Illinois – Macoupin County: $197,600 | $111/sq ft | 1,783 sq ft | random: 0.577272
Illinois – Jackson County: $154,300 | $105/sq ft | 1,463 sq ft | random: 0.474863
Illinois – Whiteside County: $283,700 | $136/sq ft | 2,087 sq ft | random: 0.823333
Illinois – Knox County: $205,100 | $118/sq ft | 1,740 sq ft | random: 0.807329
Illinois – Stephenson County: $235,600 | $140/sq ft | 1,682 sq ft | random: 0.808583
Illinois – Macon County: $212,900 | $128/sq ft | 1,659 sq ft | random: 0.681882
Illinois – Henry County: $257,700 | $123/sq ft | 2,087 sq ft | random: 0.681556
Illinois – Rock Island County: $166,300 | $127/sq ft | 1,305 sq ft | random: 0.470319
Illinois – Peoria County: $187,900 | $131/sq ft | 1,434 sq ft | random: 0.5668
Indiana – Grant County: $219,500 | $116/sq ft | 1,898 sq ft | random: 0.380158
Indiana – Henry County: $235,000 | $148/sq ft | 1,588 sq ft | random: 0.297802
Indiana – Wayne County: $203,800 | $141/sq ft | 1,441 sq ft | random: 0.246303
Indiana – Vigo County: $165,800 | $122/sq ft | 1,362 sq ft | random: 0.887793
Indiana – Madison County: $229,100 | $187/sq ft | 1,224 sq ft | random: 0.357066
Indiana – Delaware County: $221,600 | $134/sq ft | 1,651 sq ft | random: 0.943899
Indiana – Howard County: $304,300 | $152/sq ft | 1,996 sq ft | random: 0.772057
Michigan – Bay County: $145,100 | $117/sq ft | 1,239 sq ft | random: 0.252858
Ohio – Jefferson County: $246,500 | $136/sq ft | 1,814 sq ft | random: 0.250615
Ohio – Marion County: $149,700 | $116/sq ft | 1,296 sq ft | random: 0.51351
Ohio – Trumbull County: $243,000 | $133/sq ft | 1,827 sq ft | random: 0.343069
Ohio – Seneca County: $211,900 | $168/sq ft | 1,263 sq ft | random: 0.883519
Ohio – Darke County: $160,800 | $114/sq ft | 1,416 sq ft | random: 0.93315
Ohio – Scioto County: $204,200 | $131/sq ft | 1,562 sq ft | random: 0.748775
Ohio – Belmont County: $172,500 | $101/sq ft | 1,710 sq ft | random: 0.427875
Ohio – Sandusky County: $253,900 | $146/sq ft | 1,738 sq ft | random: 0.562957
Ohio – Clark County: $240,500 | $137/sq ft | 1,752 sq ft | random: 0.300112
Ohio – Columbiana County: $241,400 | $164/sq ft | 1,469 sq ft | random: 0.099072
Ohio – Richland County: $248,900 | $132/sq ft | 1,880 sq ft | random: 0.780628
Data Analysis and Descriptive Statistics
The following descriptive statistics were calculated from the 30-observation sample:
Listing Price: Mean = $215,563.33 | Median = $214,850 | Standard Deviation = $40,461.10
Square Footage: Mean = 1,637.57 sq ft | Median = 1,645.5 sq ft | Standard Deviation = 242.34 sq ft
Regional Factors Affecting the Sample
Regional samples can sometimes differ significantly from the national market due to specific local factors such as job growth, economic conditions, and demographics. For example, some regions may have higher demand for housing due to a booming local economy, while others may struggle with high unemployment rates or population decline.
The types of housing available in a region can also affect its real estate market. Some regions may have a higher proportion of single-family homes, while others may have more apartments or condominiums. This can lead to differences in average listing prices, price per square foot, and square footage compared to the national market.
Local markets can experience unique trends that may not be present at the national level. For example, a particular region may be experiencing a surge in demand due to new industries moving in or an influx of residents from other parts of the country. This can lead to a regional market that is growing faster or slower than the national housing market.
The sample consists of 30 data points, which may not be sufficient to draw comprehensive conclusions about the national market. A larger sample size, including more diverse regions, would be needed to make more accurate comparisons.
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