The median monthly rent for all apartment types in the U.S. has stabilized since 2022, despite some seasonal fluctuations. In January 2026, the monthly rent for a two-bedroom apartment amounted to ***** U.S. dollars. That was an increase from ***** U.S. dollars in January 2021, but a decline from the peak value of ***** U.S. dollars in August 2022. Where are the most expensive apartments in the U.S.? Apartment rents vary widely from state to state. To afford a two-bedroom apartment in California, for example, a renter needed to earn an average hourly wage of nearly ** U.S. dollars. This was approximately double the average wage in Michigan and 2.6 times as much as the average wage in Arkansas, South Dakota, and West Virginia. In fact, rental costs were considerably higher than the hourly minimum wage in all U.S. states. How did rents change in different states in the U.S.? In late 2025, some of the most expensive states to rent an apartment only saw a moderate increase in rental prices. Nevertheless, rents increased in about half of U.S. states as of January 2026. In North Dakota, the annual rental growth was the highest, at almost **** percent.

This dataset provides comprehensive information on the average income and rent in various states across the United States for the year 2022. It aims to offer insights into state-level economic trends and housing market dynamics.

Column Descriptions:

Region: Name of the state within the United States.

Average_Rent: Description: Average monthly rent for residential properties in each state, reflecting prevailing rental costs.

Average_Income: Average per capita income within each state, representing the average earnings of individuals residing in the state over the year.

Rents in the United States declined year-on-year for the first time in June 2023, after surging for two years in a row. In November 2021, rents soared by over ** percent annually — the highest increase on record, and in August 2022, the average rental price reached an all-time high of over ***** U.S. dollars. Rental growth has since mellowed, with January 2026 recording a decline of about **** percent from the same period one year ago. Despite the softening of the market, many states still experienced rising rents.

This dataset consolidates Rent prices in different towns and cities in Massachusetts, house prices and other relevant information. The main objective of the app is to prioritize towns in MA where house price and rent relationship is the most favorable for a potential investor.

Average Fair Market Rent Prices information was scraped from https://www.rentdata.org/states/massachusetts/ from 2006 to 2022. Massachusetts has the 3rd highest rent in the country out of 56 states and territories.

Annual Estimates of the Resident Population: April 1, 2010 to July 1, 2019. U.S. Census Bureau Population Division. May 21, 2020. The data is available for 351 towns in MA.

Home prices in MA were scraped from Boston Magazine web portal: https://www.bostonmagazine.com/property/single-family-home-price-chart-2021/. SOURCES: Boston neighborhood and town median home prices, sales volumes, and days on market provided by the Massachusetts Association of Realtors (marealtor.com) and MLS Property Information Network (mlspin.com).

Massachusetts is the second wealthiest state in the United States of America, with a median household income of $77,378 (as of 2019). The income per household per town was retrieved from https://en.wikipedia.org/wiki/List_of_Massachusetts_locations_by_per_capita_income.

The median rent for one- and two-bedroom apartments in Los Angeles, California, amounted to about ***** U.S. dollars in January 2026. Rents soared during the COVID-19 pandemic, with rental growth hitting **** percent in March 2022. This trend has since reversed, with growth turning negative in May 2023. Among the different states in the U.S., California ranks as the second most expensive rental market after Hawaii.

United States US: Price to Rent Ratio: sa data was reported at 133.313 2015=100 in 2024. This records an increase from the previous number of 133.000 2015=100 for 2023. United States US: Price to Rent Ratio: sa data is updated yearly, averaging 89.775 2015=100 from Dec 1970 (Median) to 2024, with 55 observations. The data reached an all-time high of 137.248 2015=100 in 2022 and a record low of 89.775 2015=100 in 1997. United States US: Price to Rent Ratio: sa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.AHPI: House Price Index: Seasonally Adjusted: OECD Member: Annual. Nominal house prices divided by rent price indices

Weekly and Average Rent in Rented Private Households where the Head of the Household moved to the State in the Year Leading up to Census 2022 .hidden { display: none }

The house price to rent ratio index in the U.S. declined in the second half of 2022 and remained stable until the end of 2024, indicating that house price growth slowed down compared to rental growth. At its peak, in the second quarter of 2022, the index stood at *****. House prices increased dramatically since the coronavirus pandemic. Meanwhile, rents have grown notably, but at a slower rate. What does the house price to rent ratio index measure? The house-price-to-rent-ratio measures the evolution of house prices compared to rents. It is calculated by dividing the median house price by the median annual rent. In this statistic, the values have been normalized with 100 equaling the 2015 ratio. Consequentially, a value under 100 means that rental rates have risen more than house prices. Compared to the OECD countries average, the gap between house prices and rents in the United States was wider. The house price to rent ratio in different countries The house price to rent ratio in the United Kingdom continued to increase in the second half of 2022, but growth softened, as the housing market cooled. On the other hand, the index in Germany fell drastically between the second quarter of 2022 and the second quarter of 2023. A similar trend was observed in France.

Weekly and Average Rent in Rented Private Households where the Head of the Household moved to the State in the Year Leading up to Census 2022

These data were developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. .
For a deep dive into the data model including every specific metric, see the ACS 2018-2022 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e22Estimate from 2018-22 ACS_m22Margin of Error from 2018-22 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_22Change, 2010-22 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2018-2022). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2018-2022Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/3b86ee614e614199ba66a3ff1ebfe3b5/about

Rents for industrial real estate in the U.S. have increased since 2017, with flexible/service space reaching the highest price per square foot in 2024. In just a year, the cost of, flex/service space rose by nearly *****U.S. dollars per square foot. Manufacturing facilities, warehouses, and distribution centers had lower rents and experienced milder growth. Los Angeles, Orange County, and Inland Empire, California, are some of the most expensive markets in the country. Office real estate is pricier Industrial real estate is far from being the most expensive commercial property type. For instance, average rental rates in major U.S. metros for office space are much higher than those for industrial space. This is most likely because office units are generally located in urban areas where there is limited space and thus higher demand, whereas industrial units are more suited to the outskirts of such urban areas. Industrial units, such as warehouses or factories, require much more space because they need to house large, heavy equipment or serve as a storage unit for future shipments. Big-box distribution space is gaining in importance Warehouses and distribution may currently command the lowest average rent per square foot among industrial space types, but the growing popularity of the asset class has earned it considerable gains over the past years. In 2021 and 2022, high occupier demand and insufficient supply led to soaring taking rent of big-box buildings. During that time, the vacancy rate of distribution centers fell below ****percent. The development of industrial and logistics facilities has accelerated since then, with the new supply coming to market, causing the vacancy rate to increase and the pressures on rent to ease.

Weekly and Average Rent in Rented Private Households where the Head of the Household moved to the State in the Year Leading up to Census 2022

A more recent version of these indicators can be found on this page: https://www.data.gouv.fr/fr/datasets/carte-des-loyers-indicateurs-de-loyers-dannonce-par-commune-en-2022/ Due to the evolution of the methodology and the communal mesh size, successive versions of the indicators cannot be compared to provide information on the evolution of rents.

Context of the project

Knowledge of the level of rents is important to ensure the proper functioning of the rental market and the conduct of national and local housing policies. The Directorate-General for Planning, Housing and Nature (DGALN) launched in 2018 the “rent map” project by partnering on the one hand with a research team in economics of Agrosup Dijon and the National Institute of Research in Agronomics (INRAE), and on the other hand with SeLoger, leboncoin and PAP.

This innovative partnership has rebuilt a database with more than 9 million rental ads. On the basis of these data, the research team developed a methodology for estimating indicators, at the communal scale, of rent (including charges) per m² for apartments and houses.

These experimental indicators are put online in order to be usable by all: state services, local authorities, real estate professionals, private donors and tenants. In a second phase of the project, the methodology will need to be consolidated and sustained, in order to provide for a regular update of these indicators.

This project provides additional information to that offered by the Local Land Observatorys (OLL), deployed since 2013 and reinforced since 2018 by the Elan law. Today, this associative network of 30 OLL publishes every year precise information on the rents practiced in 51 of the main French agglomerations.

Presentation of the dataset

The data disseminated are indicators of ad rents, at the level of the municipality. The field covered is the whole of France, outside of Mayotte. The geography of the municipalities is the one in force on 1 January 2017.

Rent indicators are calculated using ad data published on leboncoin, SeLoger and PAP over the period 2015-2019.

Rent indicators are provided including charges for standard properties leased in the 3 rd quarter of 2018 with the following reference characteristics: — For an apartment: 49 m² and average area per room of 22.1 m² — For a house: 92 m² area and average area per room of 22.5 m²

Data terms and conditions

These indicators can be freely used, provided that the source is indicated as follows: “UMR 1041 CESAER estimates (AgroSup Dijon-INRAE) from SeLoger, leboncoin, PAP”.

Precautions for use

Rent indicators are calculated including charges, on ad data, so measure flow rents only. The data were duplicated but could not rely on very discriminating photos and characteristics. For municipalities with no housing leased through an advertisement on at least one of the three sites during the period considered, the rent indicator is that estimated for a larger grid comprising neighbouring municipalities with similar characteristics.

Moreover, since the data do not make it possible to distinguish with certainty furnished and tourist rentals, biases in the rent indicators can be observed locally.

Users are advised to consider rent indicators with caution in municipalities where the coefficient of determination (R2) is less than 0.5, the number of observations in the municipality is less than 30 or the prediction interval is very wide.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2022 American Community Survey 1-Year Estimates.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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2022 American Community Survey 1-Year Estimates.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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2022 American Community Survey 1-Year Estimates.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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

The monthly rent of mobile homes in the U.S. has gradually increased since 2010, peaking in 2025. In the second quarter of that year, the average monthly rent for manufactured homes was *** U.S. dollars. Apartment rents soared in 2022, followed by a decline in the next three years. Where in the U.S. are manufactured homes most popular? States with a growing economy and large population provide the best opportunities for manufactured housing. In September 2025, Texas had the highest number of mobile homes in the United States. Other states with a high number of mobile homes were North Carolina and Florida. Moreover, Texas also boasted the highest number of manufactured home production plants. Affordability of mobile homes across the U.S. Manufactured homes are considerably less expensive than regular homes, which makes them an attractive option for people looking to purchase property without breaking the bank. Mobile homes are cheaper because manufacturers benefit from economies of scale due to large-scale production, which allows them to lower costs per unit. Additionally, mobile homes lose value faster than traditional homes, which can make them more affordable to purchase initially. The average sales price for a new mobile home has been on the rise, but during the housing boom in 2021, it increased dramatically.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates.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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2018-2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

📣 **FOREWORD **

I used public data available in different formats from different sources within the City-Council Statistics dpt., the BCNOpenData Web and the Web Project Inside Airbnb to create this Datasets with the intention of representing yearly series of all 73 Barcelona city’s neighbourhoods including variables historically related to displacement and neighbourhood change phenomena.

BCN Dataset Features

• year: Year the data was taken, the range goes from 2015 to 2019
• neighbourhood: Each of the 73 neighbourhoods of Barcelona
• housing(m2): Area devoted to housing as per registered activity
• parking(m2): Area devoted to parking as per registered activity
• comerce(m2): Area devoted to commerce as per registered activity
• industry(m2): Area devoted to industry as per registered activity
• offices(m2): Area devoted to offices as per registered activity
• education(m2): Area devoted to education as per registered activity
• healthcare(m2): Area devoted to healthcare as per registered activity
• hostelry(m2): Area devoted to hostelry as per registered activity
• sports(m2): Area devoted to sports as per registered activity
• religious(m2): Area devoted to religious as per registered activity
• shows(m2): Area devoted to shows as per registered activity
• other uses(m2): Area devoted to other uses as per registered activity
• avg €/€/month: Average rent price per month
• avg €/m2 Average rent prices per squared meter
• avg housing(m2): Average Household size in squared meters
• new contracts 1000 hab: Number of New rent contracts per thousand inhabitants
• expired contracts 1000 hab: Number of Terminated rent contracts per thousand inhabitants
• win/lost rents 1000 hab: Difference of new minus terminated rent contracts per thousand inhabitants
• binary rent growth_1000_hab: Same as preceeding but in binary; 0 is negative rent growth whereas 1 represents positive rent growth
• population: Population censed in each neighbourhood
• % spaniards: Percentage of locals
• % strangers: Percentage of strangers
• % w/ higher education: Percentage of people with higher studies
• unemployed: Number of unemployed people
• gini index(%): A measure of statistical dispersion intended to represent the income inequality or the wealth inequality within a nation or a social group. (0 means perfect equality and 100 perfect inequality)
• disp income(€/year): Households disposable income average per capita. Meaning the average amount of money left over that every person per household has, after paying taxes (a.k.a Net Income).
• new household purchases: Number of registered purchases of new (less than five years old) households
• protected household purchases: Number of registered purchases of public housing households
• used household purchases: Number of registered purchases of used households
• new household purchases(x1000€): Average price (in thousands of €) of registered purchases of new (less than five years old) households
• Used household purchases(x1000€): Average price (in thousands of €) of registered purchases of used households
• Total household purchases(x1000€): Average price (in thousands of €) of total registered household purchases

The bcn_datasets can be conceptually divided in three categories:

1. Housing Factors: Here we include terms relative to the rent market as well as the real state market. From average prices to amount of registered purchases and a rent growth rate, calculated subtracting expired contracts from newly signed contracts. Also the area of each neighbourhood classified per registered activity, that provides a clearer sense of the "Urban-Scheme" for each of the studied neighbourhoods.

2. Resident Characteristics: Where we include percentages of population by certain conditions like higher education, foreigners and unemployed.

3. Economic Info: Here we include terms relative to wealth (i.e. disposable income) and it’s inequality distribution (i.e. Gini index).

The Airbnb dataset originally contained all features available from the listings in barcelona during the period 2009 - 2022 . All features where removed except for the basic ones related with the subject of this study. Like Price, License, Host id. , Flat id., First and Last review date, Neighbourhood and a few more. The intention was to carry an EDA separately and at some point in the study merge both datasets in just one. Don't know when , though 🙂

🛑 BEFORE YOU START ✋

Information Publicly available was extracted from pdfs and csv, mostly, a thorough cleaning was carried due to the poor coherence of Data Entry over the years (mostly typos) but still data from rent prices for years 2012-2013 is missing. KNN imputation might do the trick in most cases.

Also I ...

The current average price per night globally on Airbnb is $137 per night.