Regional Price Parities allow comparisons of buying power across the 50 states and the District of Columbia, or from one metro area to another, for a given year. Price levels are expressed as a percentage of the overall national level.

This file is derived from the following Yelp dataset on Kaggle: https://www.kaggle.com/datasets/yelp-dataset/yelp-dataset/data

License: Since it is derived from that dataset, see the description of that dataset for the license.

The businesses in that dataset are from the following 11 North American metropolitan areas: - Boise - Edmonton - Indianapolis - Nashville - New Orleans - Philadelphia - Reno - Santa Barbara - St. Louis - Tampa - Tucson

Other than Edmonton in Canada, the rest of the metro areas are in the United States.

The file contains two columns:
- business_id - metro_area

The business_id matches the business_id field in the yelp_academic_dataset_business.json file in the Yelp academic dataset. The metro_area column is one of the labels above based on the clustering of the businesses based on the latitude and longitude for the business in the Yelp dataset.

There are 150,346 businesses. all but 3 of the businesses are in one of the above 11 clusters based on their geographic coordinates. The following three businesses are not, and their metro_area is blank in this data file:
- wKwCbAACZRAqyZkQzeBNeg : A veterinarian in Boise ID based on other data in the Yelp data file. - g0fYqQRRKmYIfChE4jMLsg : A gas station in Bennington VT, which is not one of the metro areas. - Xr4ri0RLquaZCKE_CcVKSg : A business in Stone Harbor New Jersey.

NOTE: Yelp has made the academic dataset available in different versions over the years, and the metro areas included each year have differed, so this file only applies to the dataset here in Kaggle at the time this was posted.

This data set describes metropolitan areas in the conterminous United States, developed from U.S. Bureau of the Census boundaries of Consolidated Metropolitan Statistical Areas (CMSA) and Metropolitan Statistical Areas (MSA), that have been processed to extract the largest contiguous urban area within each MSA or CMSA.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Metropolitan Divisions subdivide a Metropolitan Statistical Area containing a single core urban area that has a population of at least 2.5 million to form smaller groupings of counties or equivalent entities. Not all Metropolitan Statistical Areas with urban areas of this size will contain Metropolitan Divisions. Metropolitan Division are defined by the Office of Management and Budget (OMB) and consist of one or more main counties or equivalent entities that represent an employment center or centers, plus adjacent counties associated with the main county or counties through commuting ties. Because Metropolitan Divisions represent subdivisions of larger Metropolitan Statistical Areas, it is not appropriate to rank or compare Metropolitan Divisions with Metropolitan and Micropolitan Statistical Areas. The Metropolitan Divisions boundaries are those defined by OMB based on the 2010 Census, published in 2013, and updated in 2017.

VITAL SIGNS INDICATOR Commute Time (T4)

FULL MEASURE NAME Commute time by employment location

LAST UPDATED April 2020

DESCRIPTION Commute time refers to the average number of minutes a commuter spends traveling to work on a typical day. The dataset includes metropolitan area, county, city, and census tract tables by place of residence.

DATA SOURCE U.S. Census Bureau: Decennial Census (1980-2000) - via MTC/ABAG Bay Area Census http://www.bayareacensus.ca.gov/transportation.htm

U.S. Census Bureau: American Community Survey Table B08536 (2018 only; by place of employment) Table B08601 (2018 only; by place of employment) www.api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) For the decennial Census datasets, breakdown of commute times was unavailable by mode; only overall data could be provided on a historical basis.

For the American Community Survey datasets, 1-year rolling average data was used for all metros, region, and county geographic levels, while 5-year rolling average data was used for cities and tracts. This is due to the fact that more localized data is not included in the 1-year dataset across all Bay Area cities. Similarly, modal data is not available for every Bay Area city or census tract, even when the 5-year data is used for those localized geographies.

Regional commute times were calculated by summing aggregate county travel times and dividing by the relevant population; similarly, modal commute time were calculated using aggregate times and dividing by the number of communities choosing that mode for the given geography. Census tract data is not available for tracts with insufficient numbers of residents.

The metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary MSAs for the nine other major metropolitan areas.

Context

I gathered this data in order to compare industry composition between metro areas.

Content

This is 2019 data from OnTheMap. On Sheet1 you'll find 35 US Metro areas and the industry composition for each. Numbers reflect "All Jobs" in a CBSA. Job totals are given as raw number, not percentage.

Acknowledgements

See OnTheMap for source data.

Inspiration

There's plenty of good stuff to be done with this data. I build a similarity index to compare the metros to one other, but you may find interesting applications by grouping by region or adding other variables to what's given.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Metropolitan Divisions subdivide a Metropolitan Statistical Area containing a single core urban area that has a population of at least 2.5 million to form smaller groupings of counties or equivalent entities. Not all Metropolitan Statistical Areas with urban areas of this size will contain Metropolitan Divisions. Metropolitan Division are defined by the Office of Management and Budget (OMB) and consist of one or more main counties or equivalent entities that represent an employment center or centers, plus adjacent counties associated with the main county or counties through commuting ties. Because Metropolitan Divisions represent subdivisions of larger Metropolitan Statistical Areas, it is not appropriate to rank or compare Metropolitan Divisions with Metropolitan and Micropolitan Statistical Areas. The Metropolitan Divisions boundaries are those defined by OMB based on the 2010 Census, published in 2013, and updated in 2017.

The U.S. Census Bureau regularly collects information for many metropolitan areas in the United States, including data on number of physicians and number (and size) of hospitals. This dataset has such information for 83 different metropolitan areas.

The U.S. Census Grids (Summary File 3), 2000: Metropolitan Statistical Areas data set contains grids of demographic and socioeconomic data from the year 2000 U.S. census in ASCII and GeoTIFF formats for 50 metropolitan statistical areas with at least one million in population. The grids have a resolution of 7.5 arc-seconds (0.002075 decimal degrees), or approximately 250 square meters. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables (population, households, and housing variables). This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

Historical Dataset of Metro Region Court is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2008-2013),Total Classroom Teachers Trends Over Years (2008-2013),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2008-2013),Asian Student Percentage Comparison Over Years (2008-2011),Hispanic Student Percentage Comparison Over Years (2008-2013),Black Student Percentage Comparison Over Years (2008-2013),White Student Percentage Comparison Over Years (2008-2011),Diversity Score Comparison Over Years (2008-2013),Free Lunch Eligibility Comparison Over Years (2008-2013)

VITAL SIGNS INDICATOR Commute Time (T3)

FULL MEASURE NAME Commute time by residential location

LAST UPDATED April 2020

DESCRIPTION Commute time refers to the average number of minutes a commuter spends traveling to work on a typical day. The dataset includes metropolitan area, county, city, and census tract tables by place of residence.

DATA SOURCE U.S. Census Bureau: Decennial Census (1980-2000) - via MTC/ABAG Bay Area Census http://www.bayareacensus.ca.gov/transportation.htm

U.S. Census Bureau: American Community Survey Form B08013 (2006-2018; place of residence; overall time) Form C08136 (2006-2018; place of residence; time by mode) Form B08301 (2006-2018; place of residence) www.api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) For the decennial Census datasets, breakdown of commute times was unavailable by mode; only overall data could be provided on a historical basis.

For the American Community Survey datasets, 1-year rolling average data was used for all metros, region, and county geographic levels, while 5-year rolling average data was used for cities and tracts. This is due to the fact that more localized data is not included in the 1-year dataset across all Bay Area cities. Similarly, modal data is not available for every Bay Area city or census tract, even when the 5-year data is used for those localized geographies.

Regional commute times were calculated by summing aggregate county travel times and dividing by the relevant population; similarly, modal commute time were calculated using aggregate times and dividing by the number of communities choosing that mode for the given geography. Census tract data is not available for tracts with insufficient numbers of residents.

The metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary MSAs for the nine other major metropolitan areas.

National Planning Framework Metropolitan Areas (derived from OSI Electoral Divisions and Small Area Boundaries)

https://data.gov.ie/dataset/electoral-divisions-osi-national-statutory-boundaries

https://data.gov.ie/dataset/small-areas-ungeneralised-osi-national-statistical-boundaries-2015

This is a series-level metadata record. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Metropolitan Divisions subdivide a Metropolitan Statistical Area containing a single core urban area that has a population of at least 2.5 million to form smaller groupings of counties or equivalent entities. Not all Metropolitan Statistical Areas with urban areas of this size will contain Metropolitan Divisions. Metropolitan Division are defined by the Office of Management and Budget (OMB) and consist of one or more main counties or equivalent entities that represent an employment center or centers, plus adjacent counties associated with the main county or counties through commuting ties. Because Metropolitan Divisions represent subdivisions of larger Metropolitan Statistical Areas, it is not appropriate to rank or compare Metropolitan Divisions with Metropolitan and Micropolitan Statistical Areas. The metropolitan division boundaries are those defined by OMB based on the 2020 Census and published in 2023.

VITAL SIGNS INDICATOR Daily Miles Traveled (T15)

FULL MEASURE NAME Per-capita vehicle miles traveled

LAST UPDATED July 2017

DESCRIPTION Daily miles traveled, commonly referred to as vehicle miles traveled (VMT), reflects the total and per-person number of miles traveled in personal vehicles on a typical weekday. The dataset includes metropolitan area, regional and county tables for per-capita vehicle miles traveled.

DATA SOURCE Federal Highway Administration: Highway Statistics Series 2015 Table HM-71; limited to urbanized areas https://www.fhwa.dot.gov/policyinformation/statistics.cfm

U.S. Census Bureau: Summary File 1 2010 http://factfinder2.census.gov

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) "Vehicle miles traveled reflects the mileage accrued within the county and not necessarily the residents of that county; even though most trips are due to local residents, additional VMT can be accrued by through-trips. City data was thus discarded due to this limitation and the analysis only examine county and regional data, where through-trips are generally less common.

The metropolitan area comparison was performed by summing all of the urbanized areas within each metropolitan area (9-nine region for the San Francisco Bay Area and the primary MSA for all others). For the metro analysis, no VMT data is available outside of other urbanized areas; it is only available for intraregional analysis purposes.

VMT per capita is calculated by dividing VMT by an estimate of the traveling population. The traveling population does not include people living in institutionalized facilities, which are defined by the Census. Because institutionalized population is not estimated each year, the proportion of people living in institutionalized facilities from the 2010 Census was applied to the total population estimates for all years."

VITAL SIGNS INDICATOR Commute Mode Choice (T1)

FULL MEASURE NAME Commute mode share by residential location

LAST UPDATED April 2020

DESCRIPTION Commute mode choice, also known as commute mode share, refers to the mode of transportation that a commuter uses to travel to work, such as driving alone, biking, carpooling or taking transit. The dataset includes metropolitan area, regional, county, city and census tract tables by place of residence.

DATA SOURCE U.S. Census Bureau: Decennial Census (1960-2000) - via MTC/ABAG Bay Area Census http://www.bayareacensus.ca.gov/transportation/Means19802000.htm

U.S. Census Bureau: American Community Survey Form B08301 (2006-2018; place of residence) www.api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) For the decennial Census datasets, the breakdown of auto commuters between drive alone and carpool is not available before 1980. "Other" includes bicycle, motorcycle, taxi, and other modes of transportation.

For the American Community Survey datasets, 1-year rolling average data was used for metros, region, and county geographic levels, while 5-year rolling average data was used for cities and tracts. This is due to the fact that more localized data is not included in the 1-year dataset across all Bay Area cities. Regional mode shares are population-weighted averages of the nine counties’ modal shares. "Auto" includes drive alone and carpool for the simple data tables and is broken out in the detailed data tables accordingly, as it was not available before 1980. “Transit” includes public operators (Muni, BART, etc.) and employer-provided shuttles (e.g., Google shuttle buses). "Other" includes motorcycle, taxi, and other modes of transportation; bicycle mode share was broken out separately for the first time in the 2006 data and is shown in the detailed data tables. Census tract data is not available for tracts with insufficient numbers of residents or workers.

The metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary MSAs for the nine other major metropolitan areas.

Educational attainment rates - high school diploma and bachelor's degree.

DATA DESCRIPTION:-

1- land_area : size in square miles

2-percent_city : percent of population in central city/cities

3-percent_senior : percent of population ≤ 65 years

4-physicians : number of professionally active physicians

5-hospital_beds : total number of hospital beds

6-graduates : percent of adults that finished high school

7-work_force : number of persons in work force in thousands

8-income : total income in 1976 in millions of dollars

9-crime_rate: Ratio of number of serious crimes by total population

10-region: geographic region according to US Census

We can see that the regions have 4 values, where:

1 = North-East

2 = North-Central

3 = South

4 = West

Real Estate Breakeven Analysis for U.S. Home Types

Buy vs Rent Comparison Across Markets

By Zillow Data [source]

About this dataset

This dataset provides a comprehensive analysis of the current real estate situation in the United States. It includes breakeven analysis charts that compare buying vs renting across major U.S. markets. This dataset contains various metrics such as home types, housing stock, price-to-income ratio, cash buyers, mortgage affordability and rental affordability to name a few. This data has been compiled using Zillow's own data along with TransUnion financing survey data and the Freddie Mac Primary Mortgage Market Survey to provide an accurate understanding of each metro area’s market health and purchasing power for buyers and renters alike. By downloading this information you can compare different regions based on size rank and other factors to get full insights regarding their potential fit for your needs or investments strategies as well as any potential risks associated with each region's housing market health

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset is for real estate professionals, owner-occupants, potential buyers and renters who are interested in understanding which U.S. markets offer the most favorable home buying or rental opportunities from a financial perspective over the long term.

The “Real Estate Breakeven Analysis for U.S Home Types” dataset contains data pulled from Zillow's current and forecasted housing market metrics across many different real estate regions in the United States including cities, counties, states, metro areas and combined statistical areas (CSAs). The data includes several measures of affordability such as median price-to-rent ratio (MedPR), median breakeven horizon (MedBE) - which refers to how long it takes to make up purchase costs when compared with renting; cash purchaser share; mortgage rate; mortgage affordability indices; rental affordability rates etc.

In order to analyze and compare buying vs renting decisions across various regions in the US this dataset provides breakeven analysis at various levels of geographies i.e., state names, region types (city/metro area/county) and show how long it will take homeowners to break even on their purchase costs when compared with renting in that region over a longer period of time using discounted cash flow methodology. This information helps people understand what type of transaction is a better fit for them by weighing short term vs long term goals accordingly by evaluating these different factors related to housing metrics carefully before making financial decisions about purchasing or renting properties in desired location(s).

To use this dataset one can use either basic filters like RegionType or RegionName or more detailed filter criteria like CountyName, City name , Metro area name , State Name etc . For example if someone wanted to look at properties available for rent only then they can apply filters based on Province Type =‘Rental’ Also one can further refine searches based on filtering them with defined SampleRate , Median Price – To – Rent Ratio …..etc . This could be useful if seekers would want only specific type of property like Condominium/Coop /Multifamily 5+ Units /Duplex Triplex listing etc …and then apply other parameters like Cash Buyers percent , Mortgage Affordability Rate….etc ..in order narrow down search results while looking at Breakeven scores /horizons in their target locations . One should take advantages of all relevant parameters while searching through data before making any decision related with owning rental properties so that they can make sure best possible investment decision given

Research Ideas

• Visualizing changes in real estate trends across regions by comparing price to rent ratios, mortgage affordability indices and cash buyers over time.
• Market segmentation analysis based on region-level market characteristics such as negative equity data, rental affordability, median house values and population size.
• Predicting housing demand within a particular region based on its breakeven horizon or price to rent ratio

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: BreakEven_2017-03.csv | Column name | Description | |:----------------|:----------------------------------------------------...

This service provides location and program data for Revitalization Areas by Block Group. Revitalization Areas are HUD-designated geographic areas authorized by Congress under provisions of the National Housing Act intended to promote "revitalization, through expanded homeownership opportunities.” HUD-owned single-family properties located in a Revitalization Areas are eligible for discounted sale through special programs, including the Asset Control Areas (ACA) Program, and the Good Neighbor Next Door (GNND) Program. Revitalization Areas are determined by comparing a block group's median household income and home ownership rate to the respective rates of the surrounding area. If the block group is located in a CBSA Metropolitan area, then the metro area is used. However, if the block group is located in a Non-Metro area, then the state rate is used.

About Dataset

This dataset provides a comprehensive view of the restaurant scene in the 13 metropolitan areas of India( 900 restaurants) . Researchers, analysts, and food enthusiasts can use this dataset to gain insights into various aspects such as dining and delivery ratings, customer reviews and preferences, popular cuisines, best-selling items, and pricing information across different cities. It enables the exploration of dining patterns, the comparison of restaurants and cuisines between cities, and the identification of trends in the food industry. This dataset serves as a valuable resource for understanding the culinary landscape and making data-driven decisions related to the restaurant business, customer satisfaction, and food choices in these metropolitan areas of India. In this dataset, we have more than 127000 rows and 12 columns, a fairly large dataset. You will be able to get hands-on experience while performing the following tasks and will be able to understand how real-world problem statement analysis is done. In Data Analysis what all things we do

Handling Missing Values Explore numerical features. Explore categorical features. Finding relations between features. You have to perform the following tasks:

Explore the Data

read the dataset understand each feature and write down the details. explore the dataset info, describe and find columns with categories, and numeric columns as well. Data Cleaning:

Deleting redundant columns. Renaming the columns. Dropping duplicates. Cleaning individual columns. Remove the NaN values from the dataset Check for some more Transformations Data Visualization:

find out few insights from dataset

• What is the average dining rating across all restaurants in the dataset
• Which metropolitan area has the highest average delivery rating
• What is the total number of dining votes received by all restaurants in each city
  • How many unique cuisines are represented in the dataset
• Which restaurant has the highest average dining rating in each city
• What is the average price of the best-selling dishes compared to the non-best-selling dishes