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library(AmesHousing)
Warning: package 'AmesHousing' was built under R version 4.4.2
library(colorspace)#introduce the data set.#Run the Cleaning scriptames_quick <-make_ordinal_ames() %>%mutate(Central_Air =fct_rev(Central_Air)) %>%#reverses factor levelsmutate(Kitchen_Qual =factor(Kitchen_Qual, ordered=FALSE)) %>%mutate(Sale_Price = Sale_Price/1000) %>%filter(Gr_Liv_Area<4500)summary(ames_quick$Kitchen_Qual)
Poor Fair Typical Good Excellent
1 70 1494 1160 202
summary(ames_quick$Gr_Liv_Area)
Min. 1st Qu. Median Mean 3rd Qu. Max.
334 1126 1442 1496 1741 4476
#Two-variable Scatterplot: Above_Grade_Square_footage and Kitchen_Qualityggplot(ames_quick, aes(y=Sale_Price, x=Gr_Liv_Area, color=Kitchen_Qual))+geom_point()+geom_smooth(method="lm")+scale_color_discrete_qualitative(palette ="Dark3") +labs(title ="Scatterplot of Living Area vs. Sale Price")
`geom_smooth()` using formula = 'y ~ x'
#Create and compare two regression modelsames_lm_Size <-lm(Sale_Price~ Gr_Liv_Area, data=ames_quick)ames_lm_2K <-lm(Sale_Price~ Gr_Liv_Area + Kitchen_Qual, data=ames_quick)summary(ames_lm_Size)
Call:
lm(formula = Sale_Price ~ Gr_Liv_Area, data = ames_quick)
Residuals:
Min 1Q Median 3Q Max
-205.12 -30.58 -1.16 23.70 328.32
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.091200 3.247361 1.26 0.208
Gr_Liv_Area 0.118124 0.002062 57.28 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 54.88 on 2925 degrees of freedom
Multiple R-squared: 0.5287, Adjusted R-squared: 0.5285
F-statistic: 3281 on 1 and 2925 DF, p-value: < 2.2e-16
summary(ames_lm_2K)
Call:
lm(formula = Sale_Price ~ Gr_Liv_Area + Kitchen_Qual, data = ames_quick)
Residuals:
Min 1Q Median 3Q Max
-227.73 -23.74 0.17 22.73 320.73
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.007599 43.082192 0.070 0.944349
Gr_Liv_Area 0.085370 0.001793 47.606 < 2e-16 ***
Kitchen_QualFair -1.577103 43.332482 -0.036 0.970970
Kitchen_QualTypical 23.327105 43.041029 0.542 0.587878
Kitchen_QualGood 68.255003 43.051090 1.585 0.112974
Kitchen_QualExcellent 161.773835 43.157996 3.748 0.000181 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 43.03 on 2921 degrees of freedom
Multiple R-squared: 0.7107, Adjusted R-squared: 0.7102
F-statistic: 1435 on 5 and 2921 DF, p-value: < 2.2e-16
Figure 1: Scatterplot of Living Area vs. Sale Price
In essence, this code:
Loads and prepares the Ames Housing dataset.
Creates a scatterplot to visualize the relationship between living area, kitchen quality, and sale price.
Builds and compares two linear regression models to predict sale price, one using only living area and the other using both living area and kitchen quality.
Write three short reports, each about 300 words (as clearly labelled sections inside your same RMarkdown File - you don’t need three separate documents). Be sure to pay attention to the audience and the type of report in each case.
Warning: package 'wesanderson' was built under R version 4.4.3
ggplot(ames_owner, aes(y=Avg_Price, x=Kitchen_Qual, fill=Home_Size)) +geom_col(alpha =0.60) +geom_text(aes(label =ifelse(Pct_Change >0, paste0("+", round(Pct_Change, 0), "%"), "")),position =position_stack(vjust = .50), size =5,fontface ="bold", color ="royalblue") +labs(title ="Average Home Sale Price by Kitchen Quality",subtitle =" Home Size (Based on Living Area)",x ="Kitchen Quality",y ="Average Sale Price ($K)",fill ="Home Size",caption ="Labels: % average sale price increase as kitchen quality improves.") +scale_fill_manual(values =wes_palette("Darjeeling1")) +theme_minimal()
Warning: Removed 3 rows containing missing values or values outside the scale range
(`geom_text()`).
Figure 2: Average Home Sale Price by Kitchen Quality
Imagine you were consulting for a potential home seller. Write a technical paper about whether the quality of a kitchen is important in the valuation of a home. Discuss the chart and the regression models at a level appropriate to this audience. Since the code is given above, you don’t need to echo the code in your report. You should, however, refer to graphs or tables as appropriate in the report, and make a new one if you’d like.
Kitchen Quality and Home Value - A Technical Report for Homeowners
Summary
This report analyzes the impact of kitchen quality on home valuation using the Ames Housing dataset. Through visual analysis and statistical modeling, we found that higher quality kitchens consistently command higher sale prices, with variations in the magnitude of the increase observed across different home sizes. We offer considerations for kitchen improvements and discuss study limitations.
Background
Kitchen quality significantly influences perceived home value. Understanding this impact helps homeowners optimize their return on investment.
Methodology
We used the Ames Housing dataset, cleaning and preprocessing it. A scatterplot (Figure 1) visualized the relationship between living area, kitchen quality, and sale price. Additionally, a bar graph (Figure 2) was generated to show the average sale prices (in thousands of dollars) and the percentage increase between kitchen qualities, categorized by home size. Linear regression models were used, including one that quantified the impact of kitchen quality on sale price, controlling for living area, and another that predicted sale price based only on living area, to facilitate a comparative analysis.
Results
Visual analysis of the scatterplot (Figure 1) reveals a positive correlation between living area and sale price. Notably, homes with higher-quality kitchens consistently command higher sale prices, even when compared within similar living area ranges. Figure 2 illustrates that while average sale prices increase with better kitchen quality across all home sizes, the percentage increase varies depending on home size. For example, Figure 2 shows a trend of larger percentage increases in average sales price for larger homes when upgrading the kitchen. Conversely, smaller homes show smaller percentage increases. Average sale prices (in thousands of dollars) and the percentage increase between kitchen qualities, categorized by home size, are further illustrated in Figure 2.
Statistical modeling, using linear regression, was conducted to compare the impact of kitchen quality on sale price. Two models were employed: one predicting sale price based solely on living area, and another that included kitchen quality as a predictor, controlling for living area. The model incorporating kitchen quality confirmed a statistically significant relationship, demonstrating that kitchen quality explains a large portion of the variance in sales prices beyond living area alone. This analysis further indicates that kitchen quality adds a consistent amount to the sale price, independent of the house’s square footage.
Limitations
The Ames Housing dataset is geographically limited and may not represent other markets. The linear regression model, while significant, showed statistical significance only for ‘Excellent’ kitchen quality, a discrepancy with Figure 2 visual data. Furthermore, the model’s assumption of a constant value added by kitchen quality contradicts the varying percentage increases across home sizes in Figure 2, suggesting potential interaction effects. Subjectivity in kitchen quality assessment and other unmodeled variables are additional limitations.
Considerations for Homeowners
Homeowners can consider other ways to improve the sale price, such as targeted upgrades (countertops, appliances), comprehensive renovations, staged improvements, or market research.
Conclusion
This analysis provides insights into the relationship between kitchen quality and home valuation. Understanding the data and limitations will help homeowners make informed kitchen improvement decisions.
Now, imagine you are consulting for a realtor in the area. Write a white paper about the importance of the quality of a kitchen to the valuation of a home. Discuss the chart and the regression models.
Maximizing Property Value Through Strategic Kitchen Investments
Executive Summary
Kitchen quality is a critical, yet often underappreciated, driver of home valuation. This paper leverages data from the Ames Housing dataset to demonstrate the significant financial returns associated with strategic kitchen upgrades. Through visual analysis and statistical modeling, we reveal that higher quality kitchens consistently command higher sale prices, with notable variations in the magnitude of the increase across different home sizes. Realtors can utilize these insights to provide data-driven recommendations to homeowners, maximizing property values and enhancing client satisfaction.
Background
In the competitive real estate market, maximizing property value is a primary concern for homeowners. Kitchen renovations are a proven strategy for enhancing home value, yet many realtors lack the data-driven insights to effectively guide their clients. This paper addresses this gap by providing realtors with concrete evidence of the tangible financial returns associated with kitchen upgrades, enabling them to offer informed recommendations and enhance client satisfaction.
Key Issues and Potential Elements under Consideration
To quantify the impact of kitchen quality on home value, we employed linear regression models, comparing a model predicting sale price based solely on living area to one including kitchen quality. The model incorporating kitchen quality confirmed a statistically significant relationship, demonstrating that kitchen quality explains a significant portion of the variance in sales prices. It also showed that kitchen quality adds a consistent amount to the sale price, independent of home size.
To visualize these trends, we analyzed the Ames Housing dataset, creating a scatterplot (Figure 1) showing the relationship between living area and sale price, colored by kitchen quality, and a bar graph (Figure 2) showing average sale prices and percentage increases across kitchen quality levels and home sizes. The scatterplot (Figure 1) visually confirms the positive correlation between living area and sale price, and further illustrates that higher-quality kitchens command higher sale prices within similar living area ranges. The bar graph (Figure 2) revealed that higher-quality kitchens consistently command higher sale prices, with larger homes demonstrating greater percentage increases upon kitchen upgrades. The percentage increases shown on the graph are the average percentage increase in price when moving from one kitchen quality to the next.
However, it’s important to note that the Ames Housing dataset is geographically limited to Ames, Iowa, and the linear regression model, while significant, showed statistical significance only for ‘Excellent’ kitchen quality. Additionally, the model’s assumption of a constant value added by kitchen quality contradicts the varying percentage increases observed in Figure 2, suggesting potential interaction effects between home size and kitchen quality. Realtors should consider these limitations when applying these findings to other markets.
Conclusions & Recommendations
Strategic kitchen investments offer a clear pathway to maximizing property values. Therefore, realtors should prioritize advising homeowners on the financial benefits of kitchen upgrades, particularly in larger homes. By leveraging the data-driven insights presented in this paper, realtors can confidently guide homeowners in making informed decisions. The visual and statistical evidence demonstrates the tangible financial returns associated with kitchen upgrades. While limitations such as geographic scope and model assumptions should be considered, the overall trend clearly indicates that prioritizing kitchen quality is a valuable strategy for enhancing home value and achieving real estate success.
Recommendations:
Prioritize Kitchen Upgrades: Advise homeowners to focus on kitchen improvements, especially in larger homes, to maximize potential sale price.
Data-Driven Valuations: Use Figure 1 & Figure 2 and model insights to provide data-backed valuations to clients, demonstrating the financial impact of kitchen quality.
Client Education: Use Figure 1 & Figure 2 to illustrate the financial impact of kitchen upgrades, helping clients understand the value of their investment.
Now, imagine you are presenting on this topic to fellow students in a mid-level data science/statistics course (one where they would have already learned about multiple linear regression). Write a script/speaker notes explaining how the quality of a kitchen influences the valuation of the home. Discuss the chart and the regression models (you don’t need to include them again). Your notes should be detailed enough to show what you want to say (“talk about scatterplot” is not sufficient), but may be in bullet points rather than complete sentences.
Presentation Title: Unpacking Home Valuation: The Impact of Kitchen Quality
Target Audience: Mid-level Data Science/Statistics Students (Familiar with Multiple Linear Regression)
Slide 1: Title Slide
Title: Unpacking Home Valuation: The Impact of Kitchen Quality
Selene Faire
Presentation Date
Slide 2: Introduction
“Today, we’ll explore how kitchen quality influences home valuation using the Ames Housing dataset.”
“We’ll combine visual analysis and multiple linear regression to understand this relationship.”
“We’ll also discuss the strengths and limitations of our analysis.”
Slide 3: Data and Preprocessing
“We used the Ames Housing dataset, which contains information on home sales in Ames, Iowa.”
“We preprocessed the data, handling missing values and transforming variables as needed.”
“Specifically, we converted kitchen quality into an ordinal categorical variable.”
“We also scaled the sale price to thousands of dollars for easier interpretation.”
“We removed outliers from the living area variable.”
“First, let’s look at the scatterplot of living area vs. sale price, colored by kitchen quality.”
“Notice the positive correlation between living area and sale price: larger homes tend to sell for more.”
“Also, observe that homes with higher kitchen quality (e.g., Excellent) tend to be located towards the top of the plot, even within similar living area ranges.”
“This suggests that kitchen quality plays a role in home valuation beyond just the size of the home.”
Slide 5: Visual Analysis - Bar Graph (@fig-bar)
“Next, let’s examine the bar graph of average sale price by kitchen quality, categorized by home size.”
“The bars are grouped by kitchen quality (Poor, Fair, Typical, Good, Excellent) and colored by home size (Small, Medium, Large).”
“Notice that average sale prices increase with better kitchen quality across all home sizes.”
“Pay attention to the percentage increases shown on the bars. These represent the average percentage increase in price when moving from one kitchen quality to the next.”
“Observe that larger homes tend to show greater percentage increases when upgrading the kitchen, suggesting a higher return on investment for these homes.”
Slide 6: Multiple Linear Regression Models
“To quantify the impact of kitchen quality, we built two multiple linear regression models.”
“Model 1: Sale Price ~ Living Area”
“Model 2: Sale Price ~ Living Area + Kitchen Quality”
“We compared the performance of these models to assess the contribution of kitchen quality.”
“Model 2, which included kitchen quality, had a significantly higher R-squared value, indicating a better fit.”
“The kitchen quality variable was statistically significant, demonstrating its impact on sale price, even after controlling for living area.”
“The model showed that kitchen quality adds a consistent amount to the sale price, independent of the house’s square footage.”
“However, the model showed statistical significance only for ‘Excellent’ kitchen quality.”
Slide 7: Limitations and Considerations
“The Ames Housing dataset is geographically limited to Ames, Iowa, and may not generalize to other markets.”
“The linear regression model assumes a linear relationship between kitchen quality and sale price, which may not fully capture the complexities of real estate valuation.”
“The assessment of kitchen quality is subjective and may vary across individuals.”
“The model’s assumption of a constant value added by kitchen quality contradicts the varying percentage increases observed in Figure 2, suggesting potential interaction effects between home size and kitchen quality.”
“The statistical significance of the linear regression model for only ‘Excellent’ kitchen quality is a discrepancy with Figure 2 ’s visual data.”
Slide 8: Conclusion
“Kitchen quality significantly influences home valuation, even after controlling for living area.”
“Visual analysis and multiple linear regression provide complementary insights into this relationship.”
“Realtors can use these findings to provide data-driven recommendations to homeowners, maximizing property values.”
“However, it’s important to consider the limitations of our analysis when applying these findings to other contexts.”
Slide 9: Q&A
Open the floor for questions from the audience.
Ask MS Copilot (see separate video and mini-lab) to make a set of bullet points explaining the importance of an excellent kitchen to home sales. Once you get a general answer, ask it if Iowa (or Ames, Iowa) adds any extra challenges. Document it like the mini-lab asks you to (this will be a separate doc from your Quarto file, I’d imagine).
Appendix A: Conversation with Microsoft Copilot
This appendix includes the full transcript of the conversation with Microsoft Copilot, which was used to gather information on the importance of excellent kitchens to home sales in Ames, Iowa.
Prompt: make a set of bullet points explaining the importance of an excellent kitchen to home sales.
Response: Sure! Here are some key points highlighting the importance of an excellent kitchen in home sales:
First Impressions: The kitchen is often the heart of the home and one of the first places potential buyers look at. A modern, well-designed kitchen can create a strong positive impression.
Functionality: A well-equipped kitchen with ample storage, counter space, and modern appliances can make daily tasks easier and more enjoyable, appealing to buyers who love to cook or entertain.
Aesthetic Appeal: Stylish kitchens with high-quality finishes and contemporary designs can significantly enhance the overall look and feel of a home, making it more attractive to buyers.
Value Addition: Upgraded kitchens can increase the market value of a home, often providing a high return on investment for sellers.
Lifestyle Fit: Buyers often look for kitchens that fit their lifestyle, whether it’s for family meals, hosting gatherings, or gourmet cooking. A versatile kitchen can cater to a wide range of needs.
Energy Efficiency: Modern kitchens with energy-efficient appliances and sustainable materials can appeal to environmentally conscious buyers, adding another layer of desirability.
Competitive Edge: In a competitive real estate market, an excellent kitchen can set a home apart from others, making it more likely to sell quickly and at a better price.
Do you have any specific features in mind for your kitchen upgrade?
Prompt: does Iowa (or Ames, Iowa) adds any extra challenges.
Response: Yes, there are some specific challenges to consider when selling a home in Iowa, including Ames:
Low Inventory: Iowa, including Ames, has faced historically low home inventory[1]. This means fewer homes are available for sale, which can create a competitive market for buyers.
Rising Prices: Home prices in Ames have been increasing. For example, the median sale price in Ames was up 19.6% in January 2025 compared to the previous year[2]. This can be a double-edged sword, as higher prices might deter some buyers but can also mean higher returns for sellers.
Longer Time on Market: Homes in Ames are taking longer to sell. On average, homes sold after 50 days on the market in January 2025, compared to 32 days the previous year[2]. This could mean a longer wait for sellers to close a deal.
Supply Chain Issues: Like many places, Iowa has experienced supply chain problems and a worker shortage, impacting new construction and renovations[1]. This can affect the availability and cost of materials for home improvements.
Economic Factors: Broader economic conditions, such as interest rates and employment trends, can also impact the housing market in Iowa. For instance, rising interest rates can make mortgages more expensive, potentially reducing the pool of buyers[1].
Despite these challenges, the market in Iowa has shown resilience and growth, with increasing inventory and sales prices[3]. If you’re considering selling your home, focusing on key upgrades like an excellent kitchen can help make your property more attractive to buyers.
Is there a specific aspect of the home-selling process you’re concerned about?
Write a short reflection (about 200-300 words) explaining how the audience and mode led to differences in the three reports and what you got from the AI.
This assignment was a really interesting exercise in adapting the same data analysis for three very different audiences: homeowners, real estate pros, and fellow data science students. It really drove home how much the audience impacts not just what you say, but how you say it.
For the technical report, aimed at homeowners, I found myself walking a tightrope. It needed to be technically sound, like a proper report, but also readable for someone who might not be knee-deep in statistics. I had to explain things like regression models without getting too bogged down in jargon, which was a good challenge. I think it made me a better communicator, forcing me to really think about how to make complex ideas digestible.
Then there was the white paper for realtors. That was all about getting to the point and showing the practical value of the analysis. I focused on the bottom line – how kitchen upgrades impact home values – and kept the language clear and persuasive. It was less about the ‘how’ and more about the ‘so what,’ which is what I think realtors would care about most.
The presentation script for my classmates was a chance to really dig into the ‘how.’ I wanted to explain the analysis step-by-step, making sure everyone understood the methods and assumptions. It was more like a conversation with peers, so the tone was more relaxed and explanatory.
I also used Microsoft Copilot to get some real-world context, especially for the white paper. It was helpful to see what factors are currently impacting home sales in Ames, Iowa, and how kitchens fit into that picture. It was a good reminder that AI can be a useful tool, but you always need to double-check and think critically about the information it provides.
Overall, this assignment really highlighted the importance of knowing your audience. It’s not just about having the data; it’s about presenting it in a way that resonates with the people you’re trying to reach. And working with AI tools like Copilot showed me both their potential and their limitations in the data science process.
