The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Russia was last recorded at 20 percent. This dataset provides the latest reported value for - Russia Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

****Dataset Overview**** This dataset contains historical macroeconomic data, featuring key economic indicators in the United States. It includes important metrics such as the Consumer Price Index (CPI), Retail Sales, Unemployment Rate, Industrial Production, Money Supply (M2), and more. The dataset spans from 1993 to the present and includes monthly data on various economic indicators, processed to show their rate of change (either percentage or absolute difference, depending on the indicator).

provenance

The data in this dataset is sourced from the Federal Reserve Economic Data (FRED) database, hosted by the Federal Reserve Bank of St. Louis. FRED provides access to a wide range of economic data, including key macroeconomic indicators for the United States. My work involved calculating the rate of change (ROC) for each indicator and reorganizing the data into a more usable format for analysis. For more information and access to the full database, visit FRED's website.

Purpose and Use for the Kaggle Community:

This dataset is a valuable resource for data scientists, economists, and analysts interested in understanding macroeconomic trends, performing time series analysis, or building predictive models. With the rate of change included, users can quickly assess the growth or contraction in these indicators month-over-month. This dataset can be used for:

• Exploratory Data Analysis (EDA): Understanding historical economic trends. -Time Series Forecasting: Building models to predict future economic conditions. -Macroeconomic Analysis: Analyzing the relationship between various economic indicators. -Machine Learning Projects: Using the data as features to predict financial or economic outcomes. -By utilizing this dataset, users can perform in-depth analysis on the impact of macroeconomic changes, compare the historical performance of various indicators, and experiment with different time series forecasting techniques.

****Column Descriptions****

Year: The year of the observation.

Month: The month of the observation (1-12).

Industrial Production: Monthly data on the total output of US factories, mines, and utilities.

Manufacturers' New Orders: Durable Goods: Measures the value of new orders placed with manufacturers for durable goods, indicating future production activity.

Consumer Price Index (CPIAUCSL): A measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.

Unemployment Rate: The percentage of the total labor force that is unemployed but actively seeking employment.

Retail Sales: The total receipts of retail stores, indicating consumer spending and economic activity.

Producer Price Index: Measures the average change over time in the selling prices received by domestic producers for their output.

Personal Consumption Expenditures (PCE): A measure of the prices paid by consumers for goods and services, used in calculating inflation.

National Home Price Index: A measure of changes in residential real estate prices across the country.

All Employees, Total Nonfarm: The number of nonfarm payroll employees, an important indicator of the labor market.

Labor Force Participation Rate: The percentage of the working-age population that is either employed or actively looking for work.

Federal Funds Effective Rate: The interest rate at which depository institutions lend reserve balances to other depository institutions overnight.

Building Permits: The number of building permits issued for residential and non-residential buildings, a leading indicator of construction activity.

Money Supply (M2): The total money supply, including cash, checking deposits, and easily convertible near money.

Personal Income: The total income received by individuals from all sources, including wages, investments, and government transfers.

Trade Balance: The difference between a country's imports and exports, indicating the net trade flow.

Consumer Sentiment: The index reflecting consumer sentiment and expectations for the future economic outlook.

Consumer Confidence: A measure of how optimistic or pessimistic consumers are regarding their expected financial situation and the economy.

Notes on Interest Rates Please note that for the Federal Funds Effective Rate (FEDFUNDS), the dataset includes the absolute change in basis points (bps), not the rate of change. This means that the dataset reflects the direct change in the interest rate rather than the percentage change month-over-month. The change is represented in basis points, where 1 basis point equals 0.01%.

Tax interest is compounded daily and interest rates are reset every 3 months. Note: Provincial land tax interest rates are not reset every three months. Provincial land tax interest rates are summarized on the provincial land tax webpage. Interest rates do not apply to the Estate Administration Tax Act, 1998. Current interest rates (April 1, 2025 to June 30, 2025): * 8% on taxes you owe to the ministry * 2% on taxes you overpaid * 5% on taxes or refunds you are eligible for as a result of a successful appeal or objection * 6% on late International Fuel Tax Agreement payments * 6% on International Fuel Tax Agreement refunds the ministry has not paid you within 90 days You can download the dataset to view the historical tax interest rates. Non-Resident Speculation Tax (NRST) (1) Interest on tax you overpaid begins to accrue 40 business days after a complete NRST rebate or refund application is received by the Ministry of Finance to the date the rebate or refund is paid. (2) On refunds you are eligible for as a result of a successful appeal or objection of a NRST refund/rebate disallowance, the interest rate is the same rate as though you had overpaid and will begin to accrue 40 business days after a complete NRST rebate or refund application is received by the Ministry of Finance to the date the rebate or refund is paid. Refunds as a result of a successful appeal or objection of NRST that was paid pursuant to a Notice of Assessment, interest will accrue at the higher appeals/objection rate, beginning to accrue from the date of payment to the date the rebate or refund is paid.

United States US: Lending Interest Rate data was reported at 3.512 % pa in 2016. This records an increase from the previous number of 3.260 % pa for 2015. United States US: Lending Interest Rate data is updated yearly, averaging 6.922 % pa from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 18.870 % pa in 1981 and a record low of 3.250 % pa in 2014. United States US: Lending Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Interest Rates. Lending rate is the bank rate that usually meets the short- and medium-term financing needs of the private sector. This rate is normally differentiated according to creditworthiness of borrowers and objectives of financing. The terms and conditions attached to these rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics and data files.; ;

The benchmark interest rate in Brazil was last recorded at 15 percent. This dataset provides - Brazil Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Iran IR: Lending Interest Rate data was reported at 18.000 % pa in 2016. This records an increase from the previous number of 14.210 % pa for 2015. Iran IR: Lending Interest Rate data is updated yearly, averaging 12.000 % pa from Dec 2004 (Median) to 2016, with 13 observations. The data reached an all-time high of 18.000 % pa in 2016 and a record low of 11.000 % pa in 2013. Iran IR: Lending Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iran – Table IR.World Bank.WDI: Interest Rates. Lending rate is the bank rate that usually meets the short- and medium-term financing needs of the private sector. This rate is normally differentiated according to creditworthiness of borrowers and objectives of financing. The terms and conditions attached to these rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics and data files.; ;

According to several empirical studies US inflation and nominal interest rates as well as the real interest rate can be described as unit root processes. These results imply that nominal interest rates and expected inflation do not move one-for-one in the long run, which is incongruent with theoretical models. In this paper we introduce a new nonlinear bivariate mixture autoregressive model that seems to fit quarterly US data (1953 : II?2004 : IV) reasonably well. It is found that the three-month Treasury bill rate and inflation share a common nonlinear component that explains a large part of their persistence. The real interest rate is devoid of this component, indicating one-for-one movement of the nominal interest rate and inflation in the long run and, hence, stationarity of the real interest rate.

The benchmark interest rate in Mexico was last recorded at 8 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Brazil Lending Rate: per Month: Pre-Fixed: Individuals: Mortgages with Market Rates: Portoseg S.A. Credito Financiamento e Investimento data was reported at 0.000 % per Month in 03 Jul 2019. This stayed constant from the previous number of 0.000 % per Month for 02 Jul 2019. Brazil Lending Rate: per Month: Pre-Fixed: Individuals: Mortgages with Market Rates: Portoseg S.A. Credito Financiamento e Investimento data is updated daily, averaging 0.000 % per Month from Jan 2012 (Median) to 03 Jul 2019, with 1817 observations. The data reached an all-time high of 0.000 % per Month in 03 Jul 2019 and a record low of 0.000 % per Month in 03 Jul 2019. Brazil Lending Rate: per Month: Pre-Fixed: Individuals: Mortgages with Market Rates: Portoseg S.A. Credito Financiamento e Investimento data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Interest and Foreign Exchange Rates – Table BR.MB011: Lending Rate: per Month: by Banks: Pre-Fixed: Individuals: Mortgages with Market Rates. Lending Rate: Daily: Interest rates disclosed represent the total cost of the transaction to the client, also including taxes and operating. These rates correspond to the average fees in the period indicated in the tables. There are presented only institutions that had granted during the period determined. In general, institutions practicing different rates within the same type of credit. Thus, the rate charged to a customer may differ from the average. Several factors such as the time and volume of the transaction, as well as the guarantees offered, explain the differences between interest rates. Certain institutions grant allowance of the use of the term overdraft. However, this is not considered in the calculation of rates of this type. It should be noted that the overdraft is a modality that has high interest rates. Thus, its use should be restricted to short periods. If the customer needs resources for a longer period, should find ways to offer lower rates. The Brazilian Central Bank publishes these data with a delay about 20 days with relation to the reference period, thus allowing sufficient time for all Financial Institutions to deliver the relevant information. Interest rates presented in this set of tables correspond to averages weighted by the values of transactions conducted in the five working days specified in each table. These rates represent the average effective cost of loans to customers, consisting of the interest rates actually charged by financial institutions in their lending operations, increased tax burdens and operational incidents on the operations. The interest rates shown are the average of the rates charged in the various operations performed by financial institutions, in each modality. In one discipline, interest rates may differ between customers of the same financial institution. Interest rates vary according to several factors, such as the value and quality of collateral provided in the operation, the proportion of down payment operation, the history and the registration status of each client, the term of the transaction, among others . Institutions with “zero” did not operate on modalities for those periods or did not provide information to the Central Bank of Brazil. The Central Bank of Brazil assumes no responsibility for delay, error or other deficiency of information provided for purposes of calculating average rates presented in this

This paper estimates a model in which persistent fluctuations in expected consumption growth, expected inflation, and their time-varying volatility determine asset price variation. The model features Epstein-Zin recursive preferences, which determine the market price of macro risk factors. Analysis of the US nominal term structure data from 1953 to 2006 shows that agents dislike high uncertainty and demand compensation for volatility risks. Also, the time variation of the term premium is driven by the compensation for inflation volatility risk, which is distinct from consumption volatility risk. The central role of inflation volatility risk in explaining the time-varying term premium is consistent with other empirical evidence including survey data. In contrast, the existing long-run risks literature emphasizes consumption volatility risk and ignores inflation-specific time-varying volatility. The estimation results of this paper suggest that inflation-specific volatility risk is essential for fitting the time series of the US nominal term structure data.

Based on a large historical panel dataset, this paper provides evidence that the government spending multiplier can be significantly higher when interest rates are at or near the zero lower bound (ZLB). We estimate multipliers that are as high as 1.5 during ZLB episodes but small and statistically indistinguishable from zero during normal times. Our results are robust to different definitions of ZLB episodes, alternative ways of identifying government spending shocks, controlling for the exchange rate regime, and other potentially important state variables. In particular, we show that the difference in multipliers is not driven by multipliers being higher during periods of economic slack.

This dataset provides information about people applying for loans, including details on their personal background, finances, and loan specifics. It's meant to help us better understand how different personal factors impact whether a loan gets approved. The data includes things like the applicant's age, income, home ownership status, job history, and credit score, along with loan details such as the loan amount, interest rate, and purpose. It also shows whether the loan was approved or denied.

Features in the dataset:

1. person_age: The applicant's age.
2. person_income: How much the applicant earns annually, in USD.
3. person_home_ownership: Whether the person owns, rents, or has a mortgage on their home.
4. person_emp_length: How long the person has been employed.
5. loan_intent: The reason for applying for the loan (e.g., EDUCATION, MEDICAL, VENTURE, DEBT CONSOLIDATION, PERSONAL).
6. loan_grade: The credit grade given to the loan (A, B, C, D, etc.).
7. loan_amnt: The amount of money the applicant is requesting for the loan, in USD.
8. loan_int_rate: The interest rate on the loan.
9. loan_percent_income: The percentage of the applicant’s income being requested as a loan.
10. cb_person_default_on_file: Whether the applicant has defaulted on any previous loans (Y/N).
11. cb_person_cred_hist_length: The length of the applicant's credit history, in years.
12. loan_status: The outcome of the loan application (0 = Rejected, 1 = Accepted).

Data Description

1 id : To uniquely identify every loan in the dataset.

2 member_id : To identify the borrower to who has applied for the loan. 3 loan_amnt : The listed amount of the loan applied for by the borrower. 4 funded_amnt : The amount that was sanctioned by the LC. 5 term : The number of payments on the loan. Values are in months and can be either 36 or 60. 6 int_rate : Interest Rate on the loan 7 installment : The monthly payment owed by the borrower if the loan originates. 8 grade : LC assigned loan grade which depends on the borrower’s credit score. 9 sub_grade : LC assigned loan subgrade 10 emp_title : The job title supplied by the Borrower when applying for the loan.* 11 emp_length : Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years. 12 home_ownership : The home ownership status provided by the borrower during registration or obtained from the credit report. Our values are: RENT, OWN, MORTGAGE, OTHER 13 annual_inc : The self-reported annual income provided by the borrower during registration. 14 verification_status : Indicates if income was verified by LC, not verified, or if the income source was verified 15 issue_d : The month which the loan was funded 16 loan_status : Current status of the loan 17 purpose : A category provided in the form of a code to indicate the purpose for the loan. 18 title : Explaining the ‘purpose’ of the loan. 19 dti : The debt to income ratio is the ratio of how much the borrower owes every month to the borrower’s income every month. 20 delinq_2yrs : The number of delinquencies(late installment payment) by the borrower in the past 2 years. 21 earliest_cr_line : The month-year the borrower's earliest reported credit line was opened 22 inq_last_6mths : Inquiries for loans made by the borrower over the past 6 months. 23 mths_since_last_delinq : Months that have passed since the borrower last missed the timely payment of installment. 24 open_acc : The number of open credit lines in the borrower’s credit file. 25 pub_rec Number of derogatory public records 26 revol_bal : Total credit revolving balance 27 revol_util : Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit. 28 total_acc : The total number of credit lines currently in the borrower's credit file 29 initial_list_status : The initial listing status of the loan. Possible values are – W(whole), F(fractional) 30 out_prncp : Remaining outstanding principal for total amount funded 31 total_pymnt : Payments received to date for the total amount funded. 32 total_rec_prncp : Principal received till date. 33 total_rec_int Interest received till date. 34 total_rec_late_fee : Late fees received to date. 35 recoveries : Total recovery procedures initiated against the borrower. 36 collection_recovery_fee : The fees collected during the recovery procedures. 37 last_pymnt_d The last month when payment was received. 38 last_pymnt_amnt : The last payment amount received. 39 next_pymnt_d : Next scheduled payment date. 40 last_credit_pull_d : The most recent month LC pulled credit for this loan 41 collections_12_mths_ex_med : Number of collections in 12 months excluding medical collections 42 mths_since_last_major_derog : Months since most recent 90-day delinquency or worse rating 43 application_type Indicates whether the loan is an individual application or a joint application with two co-borrowers 44 annual_inc_joint : The combined self-reported annual income provided by the co-borrowers during registration 45 dti_joint : A ratio calculated using the co-borrowers' total monthly payments on the total debt obligations, excluding mortgages and the requested LC loan, divided by the co-borrowers' combined self-reported monthly income 46 acc_now_delinq : The number of accounts on which the borrower is now delinquent 47 tot_coll_amt : Total collection amounts ever owed by the borrower 48 tot_cur_bal : Total current balance of all accounts owned by the borrower 49 total_rev_hi_lim : Total high credit/credit limit

Lending Club offers peer-to-peer (P2P) loans through a technological platform for various personal finance purposes and is today one of the companies that dominate the US P2P lending market. The original dataset is publicly available on Kaggle and corresponds to all the loans issued by Lending Club between 2007 and 2018. The present version of the dataset is for constructing a granting model, that is, a model designed to make decisions on whether to grant a loan based on information available at the time of the loan application. Consequently, our dataset only has a selection of variables from the original one, which are the variables known at the moment the loan request is made. Furthermore, the target variable of a granting model represents the final status of the loan, that are "default" or "fully paid". Thus, we filtered out from the original dataset all the loans in transitory states. Our dataset comprises 1,347,681 records or obligations (approximately 60% of the original) and it was also cleaned for completeness and consistency (less than 1% of our dataset was filtered out).

TARGET VARIABLE

The dataset includes a target variable based on the final resolution of the credit: the default category corresponds to the event charged off and the non-default category to the event fully paid. It does not consider other values in the loan status variable since this variable represents the state of the loan at the end of the considered time window. Thus, there are no loans in transitory states. The original dataset includes the target variable “loan status”, which contains several categories ('Fully Paid', 'Current', 'Charged Off', 'In Grace Period', 'Late (31-120 days)', 'Late (16-30 days)', 'Default'). However, in our dataset, we just consider loans that are either “Fully Paid” or “Default” and transform this variable into a binary variable called “Default”, with a 0 for fully paid loans and a 1 for defaulted loans.

EXPLANATORY VARIABLES

The explanatory variables that we use correspond only to the information available at the time of the application. Variables such as the interest rate, grade, or subgrade are generated by the company as a result of a credit risk assessment process, so they were filtered out from the dataset as they must not be considered in risk models to predict the default in granting of credit.

FULL LIST OF VARIABLES

Loan identification variables:

id: Loan id (unique identifier).

issue_d: Month and year in which the loan was approved.

Quantitative variables:

revenue: Borrower's self-declared annual income during registration.

dti_n: Indebtedness ratio for obligations excluding mortgage. Monthly information. This ratio has been calculated considering the indebtedness of the whole group of applicants. It is estimated as the ratio calculated using the co-borrowers’ total payments on the total debt obligations divided by the co-borrowers’ combined monthly income.

loan_amnt: Amount of credit requested by the borrower.

fico_n: Defined between 300 and 850, reported by Fair Isaac Corporation as a risk measure based on historical credit information reported at the time of application. This value has been calculated as the average of the variables “fico_range_low” and “fico_range_high” in the original dataset.

experience_c: Binary variable that indicates whether the borrower is new to the entity. This variable is constructed from the credit date of the previous obligation in LC and the credit date of the current obligation; if the difference between dates is positive, it is not considered as a new experience with LC.

Categorical variables:

emp_length: Categorical variable with the employment length of the borrower (includes the no information category)

purpose: Credit purpose category for the loan request.

home_ownership_n: Homeownership status provided by the borrower in the registration process. Categories defined by LC: “mortgage”, “rent”, “own”, “other”, “any”, “none”. We merged the categories “other”, “any” and “none” as “other”.

addr_state: Borrower's residence state from the USA.

zip_code: Zip code of the borrower's residence.

Textual variables

title: Title of the credit request description provided by the borrower.

desc: Description of the credit request provided by the borrower.

We cleaned the textual variables. First, we removed all those descriptions that contained the default description provided by Lending Club on its web form (“Tell your story. What is your loan for?”). Moreover, we removed the prefix “Borrower added on DD/MM/YYYY >” from the descriptions to avoid any temporal background on them. Finally, as these descriptions came from a web form, we substituted all the HTML elements by their character (e.g. “&” was substituted by “&”, “<” was substituted by “<”, etc.).

RELATED WORKS

This dataset has been used in the following academic articles:

Sanz-Guerrero, M. Arroyo, J. (2024). Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending. arXiv preprint arXiv:2401.16458. https://doi.org/10.48550/arXiv.2401.16458

Ariza-Garzón, M.J., Arroyo, J., Caparrini, A., Segovia-Vargas, M.J. (2020). Explainability of a machine learning granting scoring model in peer-to-peer lending. IEEE Access 8, 64873 - 64890. https://doi.org/10.1109/ACCESS.2020.2984412

The benchmark interest rate in Norway was last recorded at 4.25 percent. This dataset provides the latest reported value for - Norway Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset contains news headlines relevant to key forex pairs: AUDUSD, EURCHF, EURUSD, GBPUSD, and USDJPY. The data was extracted from reputable platforms Forex Live and FXstreet over a period of 86 days, from January to May 2023. The dataset comprises 2,291 unique news headlines. Each headline includes an associated forex pair, timestamp, source, author, URL, and the corresponding article text. Data was collected using web scraping techniques executed via a custom service on a virtual machine. This service periodically retrieves the latest news for a specified forex pair (ticker) from each platform, parsing all available information. The collected data is then processed to extract details such as the article's timestamp, author, and URL. The URL is further used to retrieve the full text of each article. This data acquisition process repeats approximately every 15 minutes.

To ensure the reliability of the dataset, we manually annotated each headline for sentiment. Instead of solely focusing on the textual content, we ascertained sentiment based on the potential short-term impact of the headline on its corresponding forex pair. This method recognizes the currency market's acute sensitivity to economic news, which significantly influences many trading strategies. As such, this dataset could serve as an invaluable resource for fine-tuning sentiment analysis models in the financial realm.

We used three categories for annotation: 'positive', 'negative', and 'neutral', which correspond to bullish, bearish, and hold sentiments, respectively, for the forex pair linked to each headline. The following Table provides examples of annotated headlines along with brief explanations of the assigned sentiment.

Examples of Annotated Headlines


    Forex Pair
    Headline
    Sentiment
    Explanation




    GBPUSD 
    Diminishing bets for a move to 12400 
    Neutral
    Lack of strong sentiment in either direction


    GBPUSD 
    No reasons to dislike Cable in the very near term as long as the Dollar momentum remains soft 
    Positive
    Positive sentiment towards GBPUSD (Cable) in the near term


    GBPUSD 
    When are the UK jobs and how could they affect GBPUSD 
    Neutral
    Poses a question and does not express a clear sentiment


    JPYUSD
    Appropriate to continue monetary easing to achieve 2% inflation target with wage growth 
    Positive
    Monetary easing from Bank of Japan (BoJ) could lead to a weaker JPY in the short term due to increased money supply


    USDJPY
    Dollar rebounds despite US data. Yen gains amid lower yields 
    Neutral
    Since both the USD and JPY are gaining, the effects on the USDJPY forex pair might offset each other


    USDJPY
    USDJPY to reach 124 by Q4 as the likelihood of a BoJ policy shift should accelerate Yen gains 
    Negative
    USDJPY is expected to reach a lower value, with the USD losing value against the JPY


    AUDUSD

RBA Governor Lowe’s Testimony High inflation is damaging and corrosive

    Positive
    Reserve Bank of Australia (RBA) expresses concerns about inflation. Typically, central banks combat high inflation with higher interest rates, which could strengthen AUD.

Moreover, the dataset includes two columns with the predicted sentiment class and score as predicted by the FinBERT model. Specifically, the FinBERT model outputs a set of probabilities for each sentiment class (positive, negative, and neutral), representing the model's confidence in associating the input headline with each sentiment category. These probabilities are used to determine the predicted class and a sentiment score for each headline. The sentiment score is computed by subtracting the negative class probability from the positive one.

We use Bayesian time-varying parameter structural vector autoregressions with stochastic volatility to investigate changes in reduced-form and structural correlations between inventories and either sales growth or the real interest rate in the USA during both the inter-war and post-World War II periods. We identify four structural shocks by combining a single long-run restriction to identify a permanent output shock with three sign restrictions to identify demand? and supply-side transitory shocks. We show that during both the inter-war and post-war periods the structural correlation between inventories and real interest rate conditional on identified interest rate shocks is systematically positive; the reduced-form correlation between the two series is positive during the post-war period, but in line with the predictions of theory it is robustly negative during the inter-war era; during that era the correlations between inventories and either of the two other series exhibit a remarkably strong co-movement with output at business cycle frequencies.

Analysis of ‘ Zillow Housing Aspirations Report’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/zillow-housing-aspirations-reporte on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Additional Data Products

Product: Zillow Housing Aspirations Report

Date: April 2017

Definitions

Home Types and Housing Stock

• All Homes: Zillow defines all homes as single-family, condominium and co-operative homes with a county record. Unless specified, all series cover this segment of the housing stock.
• Condo/Co-op: Condominium and co-operative homes.
• Multifamily 5+ units: Units in buildings with 5 or more housing units, that are not a condominiums or co-ops.
• Duplex/Triplex: Housing units in buildings with 2 or 3 housing units.

Additional Data Products

• Zillow Home Value Forecast (ZHVF): The ZHVF is the one-year forecast of the ZHVI. Our forecast methodology is methodology post.
• Zillow creates our negative equity data using our own data in conjunction with data received through our partnership with TransUnion, a leading credit bureau. We match estimated home values against actual outstanding home-related debt amounts provided by TransUnion. To read more about how we calculate our negative equity metrics, please see our here.
• Cash Buyers: The share of homes in a given area purchased without financing/in cash. To read about how we calculate our cash buyer data, please see our research brief.
• Mortgage Affordability, Rental Affordability, Price-to-Income Ratio, Historical ZHVI, Historical ZHVI and Houshold Income are calculated as a part of Zillow’s quarterly Affordability Indices. To calculate mortgage affordability, we first calculate the mortgage payment for the median-valued home in a metropolitan area by using the metro-level Zillow Home Value Index for a given quarter and the 30-year fixed mortgage interest rate during that time period, provided by the Freddie Mac Primary Mortgage Market Survey (based on a 20 percent down payment). Then, we consider what portion of the monthly median household income (U.S. Census) goes toward this monthly mortgage payment. Median household income is available with a lag. For quarters where median income is not available from the U.S. Census Bureau, we calculate future quarters of median household income by estimating it using the Bureau of Labor Statistics’ Employment Cost Index. The affordability forecast is calculated similarly to the current affordability index but uses the one year Zillow Home Value Forecast instead of the current Zillow Home Value Index and a specified interest rate in lieu of PMMS. It also assumes a 20 percent down payment. We calculate rent affordability similarly to mortgage affordability; however we use the Zillow Rent Index, which tracks the monthly median rent in particular geographical regions, to capture rental prices. Rents are chained back in time by using U.S. Census Bureau American Community Survey data from 2006 to the start of the Zillow Rent Index, and Decennial Census for all other years.
• The mortgage rate series is the average mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate mortgage in 15-minute increments during business hours, 6:00 AM to 5:00 PM Pacific. It does not include quotes for jumbo loans, FHA loans, VA loans, loans with mortgage insurance or quotes to consumers with credit scores below 720. Federal holidays are excluded. The jumbo mortgage rate series is the average jumbo mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate, jumbo mortgage in one-hour increments during business hours, 6:00 AM to 5:00 PM Pacific Time. It does not include quotes to consumers with credit scores below 720. Traditional federal holidays and hours with insufficient sample sizes are excluded.

About Zillow Data (and Terms of Use Information)

• Zillow is in the process of transitioning some data sources with the goal of producing published data that is more comprehensive, reliable, accurate and timely. As this new data is incorporated, the publication of select metrics may be delayed or temporarily suspended. We look forward to resuming our usual publication schedule for all of our established datasets as soon as possible, and we apologize for any inconvenience. Thank you for your patience and understanding.
• All data accessed and downloaded from this page is free for public use by consumers, media, analysts, academics etc., consistent with our published Terms of Use. Proper and clear attribution of all data to Zillow is required.
• For other data requests or inquiries for Zillow Real Estate Research, contact us here.
• All files are time series unless noted otherwise.
• To download all Zillow metrics for specific levels of geography, click here.
• To download a crosswalk between Zillow regions and federally defined regions for counties and metro areas, click here.
• Unless otherwise noted, all series cover single-family residences, condominiums and co-op homes only.

Source: https://www.zillow.com/research/data/

This dataset was created by Zillow Data and contains around 200 samples along with Unnamed: 1, Unnamed: 0, technical information and other features such as: - Unnamed: 1 - Unnamed: 0 - and more.

How to use this dataset

• Analyze Unnamed: 1 in relation to Unnamed: 0
• Study the influence of Unnamed: 1 on Unnamed: 0
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Zillow Data

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--- Original source retains full ownership of the source dataset ---

Weekly updated dataset of Barclays mortgage products including interest rates, LTVs, APRC and product fees.