Graph and download economic data for Rental Vacancy Rate for the District of Columbia (DCRVAC) from 1986 to 2024 about DC, vacancy, rent, rate, and USA.

Graph and download economic data for Home Vacancy Rate for the District of Columbia (DCHVAC) from 1986 to 2024 about DC, vacancy, housing, rate, and USA.

Graph and download economic data for Rental Vacancy Rate in the United States (RRVRUSQ156N) from Q1 1956 to Q2 2025 about vacancy, rent, rate, and USA.

Context

This data contains roughly a month of data from apartment buildings all across America owned by Equity Residential. My original goal for this project was to try to reverse engineer the algorithm that changed apartment prices daily. To that end, this dataset contains some useful features such as apartment vacancy that, at least to my knowledge, weren't present on other datasets.

This was my first web scraping project so it's a little rough around the edges. In the future I hope to restart this project but for now this is all I have. Enjoy!

Content

This dataset contains apartment data starting from June 25th 2021 to July 17th 2021. The data started with only one apartment in DC, and then branched out to include every apartment complex owned by Equity Residential. It includes data such as daily rental price, bedrooms, bathrooms, location, apartment amenities, apartment vacancy rate, day recorded, and move in date.

Acknowledgements

A special thanks to Professor Joel Davis at the University of Florida who gave me the idea to scrape all these different apartment buildings.

Inspiration

It would be neat if someone more talented than me was able to explain the fluctuations in apartment price.

Disclaimers

Due to this being my first introduction to web scraping, some key fields are missing. For instance, high vacancy apartment were more likely to offer first month free rent, or other promotions. However, I was never able to record this information. Also, halfway through this project Equity Residential changed their website design which broke my scraper, leading to gaps in the data. Finally, in the beginning this was a manual scraping project so that's why the quality of the data drops off as you get closer to June 25th.

Our extensive database contains approximately 800,000 active rental property listings from across the United States. Updated daily, this comprehensive collection provides real estate professionals, investors, and property managers with valuable market intelligence and business opportunities. Database Contents

Property Addresses: Complete location data including street address, city, state, ZIP code Listing Dates: Original listing date and most recent update date Availability Status: Currently available, pending, or recently rented properties Geographic Coverage: Properties spanning all 50 states and major metropolitan areas

Applications & Uses

Market Analysis: Track rental pricing trends across different regions and property types Investment Research: Identify high-opportunity markets with favorable rental conditions Lead Generation: Connect with property owners potentially needing management services Competitive Intelligence: Monitor listing volumes, vacancy rates, and market saturation Business Development: Target specific neighborhoods or property categories for expansion

File Format & Delivery

Organized in easy-to-use CSV format for seamless integration with data analysis tools Accessible through secure download portal or API connection Daily updates ensure you're working with the most current market information Custom filtering options available to narrow results by location, date range, or other criteria

Data Quality

Rigorous validation processes to ensure address accuracy Duplicate listing detection and removal Regular verification of active status Standardized format for consistent analysis

Subscription Benefits

Access to historical listing archives for trend analysis Advanced search capabilities to target specific property characteristics Regular market reports summarizing key trends and opportunities Custom data exports tailored to your specific business needs

AK ~ 1,342 listings AL ~ 6,636 listings AR ~ 4,024 listings AZ ~ 25,782 listings CA ~ 102,833 listings CO ~ 14,333 listings CT ~ 10,515 listings DC ~ 1,988 listings DE ~ 1,528 listings FL ~ 152,258 listings GA ~ 28,248 listings HI ~ 3,447 listings IA ~ 4,557 listings ID ~ 3,426 listings IL ~ 42,642 listings IN ~ 8,634 listings KS ~ 3,263 listings KY ~ 5,166 listings LA ~ 11,522 listings MA ~ 53,624 listings MD ~ 12,124 listings ME ~ 1,754 listings MI ~ 12,040 listings MN ~ 7,242 listings MO ~ 10,766 listings MS ~ 2,633 listings MT ~ 1,953 listings NC ~ 22,708 listings ND ~ 1,268 listings NE ~ 1,847 listings NH ~ 2,672 listings NJ ~ 31,286 listings NM ~ 2,084 listings NV ~ 13,111 listings NY ~ 94,790 listings OH ~ 15,843 listings OK ~ 5,676 listings OR ~ 8,086 listings PA ~ 37,701 listings RI ~ 4,345 listings SC ~ 8,018 listings SD ~ 1,018 listings TN ~ 15,983 listings TX ~ 132,620 listings UT ~ 3,798 listings VA ~ 14,087 listings VT ~ 946 listings WA ~ 15,039 listings WI ~ 7,393 listings WV ~ 1,681 listings WY ~ 730 listings

Grand Total ~ 977,010 listings

The occupancy rate of hotels in the United States reached ** percent in October 2024. This shows a slight increase when compared to the previous year. The low occupancy rate during 2020 was due to the impact of the coronavirus (COVID-19) pandemic on the hotel industry.

This layer shows housing occupancy, tenure, and median rent/housing value. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Homeownership rate on Census Bureau's website is owner-occupied housing unit rate (called B25003_calc_pctOwnE in this layer).

The global hotel occupancy rate reached ***percent in October 2025. The highest rates that year were recorded in July and August, at ** percent, respectively.

(DC)Bed Occupancy Rate: Emergency Department: Bengkulu: Rejang Lebong Regency在2022-10-09达0.000%，相较于2022-10-08的0.000%保持不变。(DC)Bed Occupancy Rate: Emergency Department: Bengkulu: Rejang Lebong Regency数据按每日更新，2022-01-09至2022-10-09期间平均值为0.000%，共231份观测结果。该数据的历史最高值出现于2022-10-09，达0.000%，而历史最低值则出现于2022-10-09，为0.000%。CEIC提供的(DC)Bed Occupancy Rate: Emergency Department: Bengkulu: Rejang Lebong Regency数据处于定期更新的状态，数据来源于Ministry of Health，数据归类于Indonesia Premium Database的Health Sector – Table ID.HLA020: Hospital Bed Occupancy Rate: Emergency Department: by Regency/Municipality (Discontinued)。

(DC)Bed Occupancy Rate: Emergency Department: Central Java: Cilacap Regency在2022-10-09达0.000%，相较于2022-10-08的0.000%保持不变。(DC)Bed Occupancy Rate: Emergency Department: Central Java: Cilacap Regency数据按每日更新，2021-08-06至2022-10-09期间平均值为0.000%，共370份观测结果。该数据的历史最高值出现于2021-08-06，达25.862%，而历史最低值则出现于2022-10-09，为0.000%。CEIC提供的(DC)Bed Occupancy Rate: Emergency Department: Central Java: Cilacap Regency数据处于定期更新的状态，数据来源于Ministry of Health，数据归类于Indonesia Premium Database的Health Sector – Table ID.HLA020: Hospital Bed Occupancy Rate: Emergency Department: by Regency/Municipality (Discontinued)。

This layer shows housing occupancy, tenure, and median rent/housing value. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Homeownership rate on Census Bureau's website is owner-occupied housing unit rate (called B25003_calc_pctOwnE in this layer). This layer is symbolized by the count of total housing units and the overall homeownership rate. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25002, B25003, B25058, B25077, B25057, B25059, B25076, B25078Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The occupancy rate of Marriott hotels fell dramatically across all regions in 2020 due to travel restrictions caused by the coronavirus (COVID-19) pandemic. By contrast, the global hotel chain's occupancy rate increased across most regions to an overall total of **** percent in 2024.

Reference Layer: ACS Housing Units Occupancy Variables_This layer shows housing occupancy, tenure, and median rent/housing value. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Homeownership rate on Census Bureau's website is owner-occupied housing unit rate (called B25003_calc_pctOwnE in this layer). This layer is symbolized by the overall homeownership rate. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2016-2020ACS Table(s): B25002, B25003, B25058, B25077, B25057, B25059, B25076, B25078Data downloaded from: Census Bureau's API for American Community Survey Date of API call: March 17, 2022National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

(DC)Bed Occupancy Rate: Emergency Department: Aceh: Aceh Tamiang Regency在2022-10-09达0.000%，相较于2022-10-08的0.000%保持不变。(DC)Bed Occupancy Rate: Emergency Department: Aceh: Aceh Tamiang Regency数据按每日更新，2021-08-06至2022-10-09期间平均值为0.000%，共370份观测结果。该数据的历史最高值出现于2022-10-09，达0.000%，而历史最低值则出现于2022-10-09，为0.000%。CEIC提供的(DC)Bed Occupancy Rate: Emergency Department: Aceh: Aceh Tamiang Regency数据处于定期更新的状态，数据来源于Ministry of Health，数据归类于Indonesia Premium Database的Health Sector – Table ID.HLA020: Hospital Bed Occupancy Rate: Emergency Department: by Regency/Municipality (Discontinued)。

This layer shows housing occupancy, tenure, and median rent/housing value. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Homeownership rate on Census Bureau's website is owner-occupied housing unit rate (called B25003_calc_pctOwnE in this layer). This layer is symbolized by the overall homeownership rate. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2015-2019ACS Table(s): B25002, B25003, B25058, B25077, B25057, B25059, B25076, B25078Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 10, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

(DC)Bed Occupancy Rate: Emergency Department: Bangka Belitung Islands: Bangka Regency在2022-10-09达0.000%，相较于2022-10-08的0.000%保持不变。(DC)Bed Occupancy Rate: Emergency Department: Bangka Belitung Islands: Bangka Regency数据按每日更新，2021-08-06至2022-10-09期间平均值为0.000%，共325份观测结果。该数据的历史最高值出现于2022-09-28，达25.000%，而历史最低值则出现于2022-10-09，为0.000%。CEIC提供的(DC)Bed Occupancy Rate: Emergency Department: Bangka Belitung Islands: Bangka Regency数据处于定期更新的状态，数据来源于Ministry of Health，数据归类于Indonesia Premium Database的Health Sector – Table ID.HLA020: Hospital Bed Occupancy Rate: Emergency Department: by Regency/Municipality (Discontinued)。

(DC)Bed Occupancy Rate: Emergency Department: East Java: Blitar Municipality在2022-10-09达0.000%，相较于2022-10-08的0.000%保持不变。(DC)Bed Occupancy Rate: Emergency Department: East Java: Blitar Municipality数据按每日更新，2021-08-06至2022-10-09期间平均值为0.000%，共370份观测结果。该数据的历史最高值出现于2021-08-11，达70.588%，而历史最低值则出现于2022-10-09，为0.000%。CEIC提供的(DC)Bed Occupancy Rate: Emergency Department: East Java: Blitar Municipality数据处于定期更新的状态，数据来源于Ministry of Health，数据归类于Indonesia Premium Database的Health Sector – Table ID.HLA020: Hospital Bed Occupancy Rate: Emergency Department: by Regency/Municipality (Discontinued)。

(DC)Bed Occupancy Rate: Emergency Department: Bali: Karangasem Regency在2022-10-09达0.000%，相较于2022-10-08的0.000%保持不变。(DC)Bed Occupancy Rate: Emergency Department: Bali: Karangasem Regency数据按每日更新，2021-08-06至2022-10-09期间平均值为0.000%，共370份观测结果。该数据的历史最高值出现于2021-08-24，达100.000%，而历史最低值则出现于2022-10-09，为0.000%。CEIC提供的(DC)Bed Occupancy Rate: Emergency Department: Bali: Karangasem Regency数据处于定期更新的状态，数据来源于Ministry of Health，数据归类于Indonesia Premium Database的Health Sector – Table ID.HLA020: Hospital Bed Occupancy Rate: Emergency Department: by Regency/Municipality (Discontinued)。

(DC)Bed Occupancy Rate: Emergency Department: East Java: Malang Regency在2022-10-09达0.000%，相较于2022-10-08的0.000%保持不变。(DC)Bed Occupancy Rate: Emergency Department: East Java: Malang Regency数据按每日更新，2021-08-06至2022-10-09期间平均值为1.299%，共370份观测结果。该数据的历史最高值出现于2021-08-11，达72.581%，而历史最低值则出现于2022-10-09，为0.000%。CEIC提供的(DC)Bed Occupancy Rate: Emergency Department: East Java: Malang Regency数据处于定期更新的状态，数据来源于Ministry of Health，数据归类于Indonesia Premium Database的Health Sector – Table ID.HLA020: Hospital Bed Occupancy Rate: Emergency Department: by Regency/Municipality (Discontinued)。

(DC)Bed Occupancy Rate: Emergency Department: East Java: Batu Municipality在2022-10-09达0.000%，相较于2022-10-08的0.000%保持不变。(DC)Bed Occupancy Rate: Emergency Department: East Java: Batu Municipality数据按每日更新，2021-08-06至2022-10-09期间平均值为0.000%，共370份观测结果。该数据的历史最高值出现于2021-08-06，达100.000%，而历史最低值则出现于2022-10-09，为0.000%。CEIC提供的(DC)Bed Occupancy Rate: Emergency Department: East Java: Batu Municipality数据处于定期更新的状态，数据来源于Ministry of Health，数据归类于Indonesia Premium Database的Health Sector – Table ID.HLA020: Hospital Bed Occupancy Rate: Emergency Department: by Regency/Municipality (Discontinued)。