Research Paper Undergraduate 836 words

Real Estate Market Analysis: East North Central Region

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Abstract

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.

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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.

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

Representative Data Sample

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

Data Analysis and Descriptive Statistics

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

Regional Factors Affecting the Sample

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

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

2 Locked Sections · 195 words remaining

46% of this paper shown

Sampling Methodology · 85 words

"Simple random sampling process using Excel"

Scatterplot and Regression Analysis · 110 words

"Positive price-to-square-footage relationship and prediction"

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Key Concepts in This Paper

Listing Price Square Footage Simple Random Sampling Descriptive Statistics Regional Market Price Per Square Foot Regression Trendline Housing Stock East North Central Market Divergence