Final Project

Analysis of the Denver Housing Market

Author

Kacey Barta

Analysis of the Denver Housing Market

Introduction

The rising cost of housing in major U.S. cities has sparked public concern, especially in cities like Denver, Colorado. One area of growing attention is the short-term rental market, namely Airbnb, and how its presence may impact the affordability and availability of housing. For this project, I explore how costly Airbnb listings are in Denver and identify the main factors that contribute to the price of a listing. This investigation can offer insight for both policymakers concerned with housing equity and potential Airbnb users or hosts looking to understand pricing patterns.

This analysis uses a publicly available dataset from Kaggle titled “Denver Airbnb Open Data,” part of Airbnb’s Inside Airbnb initiative. The dataset captures Airbnb activity in Denver, Colorado, and is structured across several files that include detailed information on listings, host attributes, prices, availability, and neighborhoods.

Research Question

What are the key features of an Airbnb listing in Denver that influence its price? More specifically:

How does location, room type, availability, or host characteristics relate to pricing?

Hypothesis

Airbnb listings that are entire homes or private apartments, located in desirable neighborhoods and have higher-rated hosts are priced significantly higher. These neighborhoods, often known for their safety, walkability, proximity to downtown, and access to outdoor amenities, are not only attractive to visitors but also highly sought after by Colorado locals. However, the growing prevalence of short-term rentals has driven up housing costs in these areas, making them increasingly unaffordable for long-term residents.

Supporting Analysis

Denver has experienced rapid growth and gentrification in recent years, making housing affordability a major concern for residents. Short-term rentals like Airbnb have been under scrutiny for their potential role in exacerbating housing shortages by reducing the supply of long-term rental units.

I find this topic compelling because it ties into broader urban issues like housing equity and the balance between tourism-driven revenue and residents’ needs. To strengthen the analysis and provide a more grounded context, I will also scrape and analyze affordable housing data from the City of Denver’s public Power BI dashboard. This second stage of the project will allow me to:

Evaluate whether the city’s efforts to sustain affordable housing are effectively countering the inflationary effects of a strong short-term rental market.

Exploring Airbnb Data Trends

The visualization below illustrates the price distribution by marketed room type on Airbnb.com. The most expensive Airbnbs in Denver are typically entire homes or apartments, offering guests full privacy and the comfort of a private, self-contained space.

The next visualization demonstrates a clear relationship between a host’s review score and the price of their Airbnb rental — higher-rated listings tend to command higher nightly rates. This suggests that providing clean, well-managed short-term stays for tourists and visitors to the Mile High City is financially rewarding for hosts. However, this success raises a very important question: as more properties are prioritized for short-term rental income, are long-term residents – particularly native Coloradans – being priced out of their own neighborhoods?

The next visualization illustrates that, on average, the most expensive Airbnbs in Denver are specficially entire apartments, condominiums, or homes.

Notably, apartments in particular average well over $250 per night. This supports the hypothesis presented at the beginning of the analysis that short-term rentals may be contributing to rising housing costs in Denver by reducing the availability of long-term rental options.

The visualization below highlights that the majority of Airbnb listings in Denver are advertised to have availability for periods of less than one month (about 28 days) at a time, with over 1,200 units available. In comparison, only around one third, or 400 properties, of these listings are available year-round. This significant imbalance suggests that many hosts have opted for short-term, occasional rentals rather than offering their properties as full-time housing options. The proliferation of short-term rentals may be reducing the supply of long-term housing, thereby putting upward pressure on rental prices in the Denver housing market.

Finally, I examine the relationship between Airbnb location and nightly price. Higher nightly rates consistently appear in some of Denver’s most vibrant, urban neighborhoods—areas that tend to attract young, working-class millennials. Neighborhoods like Union Station, Five Points, Highland, Berkeley, and Whittier are particularly popular, reflecting their cultural appeal and central locations. While the elevated prices in these sought-after areas are expected, they also raise concerns about affordability for young residents who wish to live in these neighborhoods but face limited housing access due to the dominance of short-term rentals like Airbnb.

Affordable Housing Efforts & Opportunities

To further explore housing affordability issues in Denver, Colorado, I analyzed data from the County of Denver’s Affordable Home Ownership Program. This program is designed to offer safe and stable housing options to moderate-income households at prices significantly lower than those found on the open market.

The goal is to showcase potential gaps in Denver’s housing affordability strategy, particularly when viewed alongside the proliferation of high-priced Airbnb listings.

To learn more about the program, please visit: https://denvergov.org/Government/Agencies-Departments-Offices/Agencies-Departments-Offices-Directory/Department-of-Housing-Stability/Resident-Resources/Affordable-Home-Ownership

For this analysis, housing is defined as affordable when a household spends no more than 30% of its income on housing costs. The properties included have no formal eligibility requirements and are accessible to the general public. However, they remain affordable for lower-income households, making them examples of Naturally Occurring Affordable Housing (NOAH).

Emerging Affordable Housing Data Patterns

The following visualizations highlights Denver’s ongoing efforts to address housing affordability through the construction and opening of affordable housing projects. While earlier decades show relatively few projects, there’s a noticeable increase in development activity from the early 2000s onward, with peaks in the mid-2000s and again in recent years.

This upward trend reflects a growing commitment by the city to provide stable housing solutions for lower- and moderate-income residents. Although these projects represent only a first step, they signal important momentum and consistency in confronting Denver’s housing crisis. Sustained investment in affordable housing is essential to ensure that long-term residents are not displaced by rising rents and the growing prevalence of short-term rentals.

Next, this visualization shows the number of restricted housing units by neighborhood in Denver, with Five Points, Central Park, and Capitol Hill leading the list. Notably, these same neighborhoods also rank among the most expensive areas for nightly Airbnb rentals, based on previous analysis.

Restricted units are designated as affordable housing with eligibility requirements, such as income verification, meaning access is limited to qualified applicants.

In essence, the concentration of both high Airbnb pricing and restricted housing in the same neighborhoods reveals a growing barrier—local residents are being priced out of the most livable and sought-after areas, unless they qualify for limited affordable housing programs. This reflects a larger tension between short-term rental profitability and long-term housing equity in Denver.

The final chart illustrates the division between city-funded affordable housing projects and those that received no direct city funding. While the majority of projects do benefit from city support, a substantial number still proceed without any municipal assistance. This underscores a critical reality: Denver’s government can only go so far in influencing housing affordability within a highly competitive and increasingly privatized real estate market.

Conclusion

This project began with a hypothesis: that Denver’s booming Airbnb market and high rental costs are making it increasingly difficult for local residents to afford housing—especially in the city’s most desirable neighborhoods. Our exploration of Airbnb data confirmed that neighborhoods with the highest nightly rates often overlap with those containing the most “restricted” affordable units, where access is dependent on income verification. This pattern indicates that lower-income residents are being priced out of prime locations, reinforcing long-standing issues of spatial inequality.

The city of Denver has clearly recognized this challenge and has responded by constructing affordable housing every year, with notable increases in development over the past two decades. These efforts demonstrate a growing commitment to providing stability and support for those most affected by rising housing costs. However, as the funding data show, a large share of projects still lack city backing, highlighting the limitations of public policy in a landscape shaped by powerful market forces.

Together, these insights point to an undeniable conclusion: Denver’s housing market is in crisis. The city must sustain, and expand, its commitment to affordable housing as a matter of justice and equity. Protecting the rights of Colorado natives and long-time residents to remain in their communities should be a top priority, ensuring that everyone has a place in the vibrant and beautiful home we all share.

--- title: "Final Project" subtitle: "Analysis of the Denver Housing Market" author: "Kacey Barta" format: html: toc: true code-tools: true embed-resources: true execute: warning: false message: false echo: false editor: visual --- ## Analysis of the Denver Housing Market ### Introduction The rising cost of housing in major U.S. cities has sparked public concern, especially in cities like Denver, Colorado. One area of growing attention is the short-term rental market, namely Airbnb, and how its presence may impact the affordability and availability of housing. For this project, I explore how costly Airbnb listings are in Denver and identify the main factors that contribute to the price of a listing. This investigation can offer insight for both policymakers concerned with housing equity and potential Airbnb users or hosts looking to understand pricing patterns. This analysis uses a publicly available dataset from Kaggle titled “Denver Airbnb Open Data,” part of Airbnb's Inside Airbnb initiative. The dataset captures Airbnb activity in Denver, Colorado, and is structured across several files that include detailed information on listings, host attributes, prices, availability, and neighborhoods. ### Research Question What are the key features of an Airbnb listing in Denver that influence its price? More specifically: How does location, room type, availability, or host characteristics relate to pricing? ### Hypothesis Airbnb listings that are entire homes or private apartments, located in desirable neighborhoods and have higher-rated hosts are priced significantly higher. These neighborhoods, often known for their safety, walkability, proximity to downtown, and access to outdoor amenities, are not only attractive to visitors but also highly sought after by Colorado locals. However, the growing prevalence of short-term rentals has driven up housing costs in these areas, making them increasingly unaffordable for long-term residents. ### Supporting Analysis Denver has experienced rapid growth and gentrification in recent years, making housing affordability a major concern for residents. Short-term rentals like Airbnb have been under scrutiny for their potential role in exacerbating housing shortages by reducing the supply of long-term rental units. I find this topic compelling because it ties into broader urban issues like housing equity and the balance between tourism-driven revenue and residents’ needs. To strengthen the analysis and provide a more grounded context, I will also scrape and analyze affordable housing data from the City of Denver’s public Power BI dashboard. This second stage of the project will allow me to: Evaluate whether the city's efforts to sustain affordable housing are effectively countering the inflationary effects of a strong short-term rental market. ```{r} #| label: loading in the libraries/data #| echo: false library(tidyverse) # For all the tidy things! library(skimr) #scanning data quickly to check for missing / strange numbers library(lubridate) # Convenient transforming of date values. library(knitr) # Useful tools when 'knitting' (rendering) Quarto documents. library(dplyr) #Load the package into the session, making all its functions available to use. library(ggplot2) #Used for visualization and graphing data denverairbnb <- read_csv("https://myxavier-my.sharepoint.com/:x:/g/personal/bartak1_xavier_edu/EZZdg31YTvJHof6ily4pXvEBBlISZ3-AHwndCdDMHTRZ-Q?download=1") ``` ### Exploring Airbnb Data Trends The visualization below illustrates the price distribution by marketed room type on Airbnb.com. The most expensive Airbnbs in Denver are typically entire homes or apartments, offering guests full privacy and the comfort of a private, self-contained space. ```{r} #| echo: false ggplot(denverairbnb, aes(x = room_type, y = price)) + geom_boxplot(fill = "skyblue") + scale_y_log10() + # Helps with skewed price distributions labs(title = "Price Distribution by Room Type", x = "Room Type", y = "Price (log scale)") + theme_minimal() ``` The next visualization demonstrates a clear relationship between a host’s review score and the price of their Airbnb rental --- higher-rated listings tend to command higher nightly rates. This suggests that providing clean, well-managed short-term stays for tourists and visitors to the Mile High City is financially rewarding for hosts. However, this success raises a very important question: as more properties are prioritized for short-term rental income, are long-term residents -- particularly native Coloradans -- being priced out of their own neighborhoods? ```{r} # Filter: only entire homes and realistic price & review score range filtered_data <- denverairbnb %>% filter(room_type == "Entire home/apt", price < 1000, # Remove extreme price outliers review_scores_rating >= 60) # Keep realistic review scores # Plot ggplot(filtered_data, aes(x = review_scores_rating, y = price)) + geom_point(alpha = 0.4, color = "#1b7837") + # more readable green geom_smooth(method = "lm", se = FALSE, color = "#d73027", size = 1) + labs( title = "Does Review Score Affect Airbnb Pricing?", subtitle = "Entire home/apt listings in Denver (Price < $1000)", x = "Review Score (0-100)", y = "Price (USD per night)" ) + theme_minimal(base_size = 13) + theme( plot.title = element_text(face = "bold"), plot.subtitle = element_text(margin = margin(b = 10)) ) ``` The next visualization illustrates that, on average, the most expensive Airbnbs in Denver are specficially entire apartments, condominiums, or homes. Notably, apartments in particular average well over \$250 per night. This supports the hypothesis presented at the beginning of the analysis that short-term rentals may be contributing to rising housing costs in Denver by reducing the availability of long-term rental options. ```{r} denverairbnb %>% group_by(property_type) %>% summarise(avg_price = mean(price, na.rm = TRUE), count = n()) %>% filter(count > 10) %>% # Filter out rare property types ggplot(aes(x = reorder(property_type, avg_price), y = avg_price)) + geom_col(fill = "coral") + coord_flip() + labs(title = "Average Price by Property Type", x = "Property Type", y = "Average Price") + theme_minimal() ``` The visualization below highlights that the majority of Airbnb listings in Denver are advertised to have availability for periods of less than one month (about 28 days) at a time, with over 1,200 units available. In comparison, only around one third, or 400 properties, of these listings are available year-round. This significant imbalance suggests that many hosts have opted for short-term, occasional rentals rather than offering their properties as full-time housing options. The proliferation of short-term rentals may be reducing the supply of long-term housing, thereby putting upward pressure on rental prices in the Denver housing market. ```{r} ggplot(denverairbnb, aes(x = availability_365)) + geom_histogram(binwidth = 30, fill = "purple", color = "white") + labs(title = "Listing Availability Throughout the Year", x = "Availability (days)", y = "Number of Listings") + theme_minimal() ``` Finally, I examine the relationship between Airbnb location and nightly price. Higher nightly rates consistently appear in some of Denver’s most vibrant, urban neighborhoods—areas that tend to attract young, working-class millennials. Neighborhoods like Union Station, Five Points, Highland, Berkeley, and Whittier are particularly popular, reflecting their cultural appeal and central locations. While the elevated prices in these sought-after areas are expected, they also raise concerns about affordability for young residents who wish to live in these neighborhoods but face limited housing access due to the dominance of short-term rentals like Airbnb. ```{r} denverairbnb %>% group_by(neighbourhood_cleansed) %>% summarise(avg_price = mean(price, na.rm = TRUE), count = n()) %>% filter(count > 100) %>% # Filter out rare property types ggplot(aes(x = reorder(neighbourhood_cleansed, avg_price), y = avg_price)) + geom_col(fill = "coral") + coord_flip() + labs(title = "Average Price by Neighborhood", x = "Property Type", y = "Average Price") + theme_minimal() ``` ```{r} library(dplyr) airbnb_counts <- denverairbnb %>% group_by(neighbourhood_cleansed) %>% summarise(listing_count = n()) %>% arrange(desc(listing_count)) ``` ### Affordable Housing Efforts & Opportunities To further explore housing affordability issues in Denver, Colorado, I analyzed data from the County of Denver’s Affordable Home Ownership Program. This program is designed to offer safe and stable housing options to moderate-income households at prices significantly lower than those found on the open market. The goal is to showcase potential gaps in Denver’s housing affordability strategy, particularly when viewed alongside the proliferation of high-priced Airbnb listings. To learn more about the program, please visit: https://denvergov.org/Government/Agencies-Departments-Offices/Agencies-Departments-Offices-Directory/Department-of-Housing-Stability/Resident-Resources/Affordable-Home-Ownership For this analysis, housing is defined as affordable when a household spends no more than 30% of its income on housing costs. The properties included have no formal eligibility requirements and are accessible to the general public. However, they remain affordable for lower-income households, making them examples of Naturally Occurring Affordable Housing (NOAH). ```{r} library(tidyverse) # The tidyverse collection of packages library(httr) # Useful for web authentication library(rvest) # Useful tools for working with HTML and XML library(lubridate) # Working with dates library(magrittr) # Piping output easily with loops library(chromote) # Remotely controlling a Chrome browser library(dplyr) ``` ```{r} den <- read_html_live("https://app.powerbigov.us/view?r=eyJrIjoiMjY4Nzc1ZDUtMjU0My00MzZkLTlhZTEtMGQwZmY4MmFhYzZiIiwidCI6IjM5Yzg3YWIzLTY2MTItNDJjMC05NjIwLWE2OTZkMTJkZjgwMyJ9") Sys.sleep(10) den$click("div[tab-order='4750']") den$click("div.mid-viewport") column_names <- c( "Project Name", "Address", "Status", "Built/Open Date", "Preserved Date*", "Restricted Units", "Supportive Units", "Rental or Sale", "Funding Category", "Neighborhood" ) # Function to scrape a visible row by row index get_row_data <- function(den, index) { selector <- paste0("div.row[row-index='", index, "']") row_data <- den %>% html_elements(selector) %>% html_elements("div.cell-unselected.pivotTableCellWrap.cell-interactive.main-cell") %>% html_text2() if (length(row_data) == length(column_names)) { return(setNames(as.data.frame(t(row_data), stringsAsFactors = FALSE), column_names)) } else { return(NULL) } } # Loop through visible rows until target is reached collect_data <- function(den, target_rows = 100) { all_data <- list() seen_indices <- c() while (length(all_data) < target_rows) { rows <- den %>% html_elements("div.mid-viewport div.row") for (r in rows) { idx <- as.integer(html_attr(r, "row-index")) if (!is.na(idx) && !(idx %in% seen_indices)) { seen_indices <- c(seen_indices, idx) row_df <- get_row_data(den, idx) if (!is.null(row_df)) all_data[[length(all_data) + 1]] <- row_df } } # Click last visible row to load more if (length(seen_indices) > 0) { last_idx <- max(seen_indices, na.rm = TRUE) den$click(paste0("div.row[row-index='", last_idx, "']")) Sys.sleep(2) } } return(bind_rows(all_data)) } # Run data collection project_data <- collect_data(den, target_rows = 100) ``` ### Emerging Affordable Housing Data Patterns The following visualizations highlights Denver’s ongoing efforts to address housing affordability through the construction and opening of affordable housing projects. While earlier decades show relatively few projects, there's a noticeable increase in development activity from the early 2000s onward, with peaks in the mid-2000s and again in recent years. This upward trend reflects a growing commitment by the city to provide stable housing solutions for lower- and moderate-income residents. Although these projects represent only a first step, they signal important momentum and consistency in confronting Denver’s housing crisis. Sustained investment in affordable housing is essential to ensure that long-term residents are not displaced by rising rents and the growing prevalence of short-term rentals. ```{r} ggplot(project_data, aes(x = Status)) + geom_bar(fill = "steelblue") + theme_minimal() + labs(title = "Number of Projects by Status", x = "Proejct Status", y = "Count") + coord_flip() ``` ```{r} ### Projects Completed per Year project_data <- project_data %>% mutate(`Built/Open Date` = mdy(`Built/Open Date`), Year_Built = year(`Built/Open Date`)) %>% filter(Year_Built >= 1980) ggplot(project_data, aes(x = Year_Built)) + geom_bar(fill = "darkgreen") + theme_minimal() + labs(title = "Projects Completed per Year (1980 and After)", x = "Year Built", y = "Number of Projects") ``` Next, this visualization shows the number of restricted housing units by neighborhood in Denver, with Five Points, Central Park, and Capitol Hill leading the list. Notably, these same neighborhoods also rank among the most expensive areas for nightly Airbnb rentals, based on previous analysis. Restricted units are designated as affordable housing with eligibility requirements, such as income verification, meaning access is limited to qualified applicants. In essence, the concentration of both high Airbnb pricing and restricted housing in the same neighborhoods reveals a growing barrier—local residents are being priced out of the most livable and sought-after areas, unless they qualify for limited affordable housing programs. This reflects a larger tension between short-term rental profitability and long-term housing equity in Denver. ```{r} project_data_clean <- project_data %>% filter(!is.na(Neighborhood), !is.na(`Restricted Units`)) %>% mutate(`Restricted Units` = as.numeric(`Restricted Units`)) %>% group_by(Neighborhood) %>% filter(sum(`Restricted Units`, na.rm = TRUE) >= 100) %>% ungroup() ggplot(project_data_clean, aes(x = reorder(Neighborhood, `Restricted Units`, sum), y = `Restricted Units`)) + geom_col(fill = "orange") + coord_flip() + theme_minimal() + labs(title = "Restricted Units by Neighborhood", x = "Neighborhood", y = "Restricted Units") ``` The final chart illustrates the division between city-funded affordable housing projects and those that received no direct city funding. While the majority of projects do benefit from city support, a substantial number still proceed without any municipal assistance. This underscores a critical reality: Denver's government can only go so far in influencing housing affordability within a highly competitive and increasingly privatized real estate market. ```{r} ggplot(project_data, aes(x = `Funding Category`)) + geom_bar(fill = "skyblue") + theme_minimal() + labs( title = "Projects by Funding Category", x = "Funding Category", y = "Number of Projects" ) + coord_flip() ``` ## Conclusion This project began with a hypothesis: that Denver’s booming Airbnb market and high rental costs are making it increasingly difficult for local residents to afford housing—especially in the city’s most desirable neighborhoods. Our exploration of Airbnb data confirmed that neighborhoods with the highest nightly rates often overlap with those containing the most "restricted" affordable units, where access is dependent on income verification. This pattern indicates that lower-income residents are being priced out of prime locations, reinforcing long-standing issues of spatial inequality. The city of Denver has clearly recognized this challenge and has responded by constructing affordable housing every year, with notable increases in development over the past two decades. These efforts demonstrate a growing commitment to providing stability and support for those most affected by rising housing costs. However, as the funding data show, a large share of projects still lack city backing, highlighting the limitations of public policy in a landscape shaped by powerful market forces. Together, these insights point to an undeniable conclusion: Denver’s housing market is in crisis. The city must sustain, and expand, its commitment to affordable housing as a matter of justice and equity. Protecting the rights of Colorado natives and long-time residents to remain in their communities should be a top priority, ensuring that everyone has a place in the vibrant and beautiful home we all share.