30 Year Mortgage Rate in the United States decreased to 6.23 percent in November 26 from 6.26 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

Fixed 30-year mortgage rates in the United States averaged 6.40 percent in the week ending November 21 of 2025. This dataset provides the latest reported value for - United States MBA 30-Yr Mortgage 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 the United States was last recorded at 4 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.

This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...).

The benchmark interest rate in Sweden was last recorded at 1.75 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

United States CSI: Expected Interest Rates: Next Yr: Go Down data was reported at 4.000 % in May 2018. This records a decrease from the previous number of 6.000 % for Apr 2018. United States CSI: Expected Interest Rates: Next Yr: Go Down data is updated monthly, averaging 11.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 54.000 % in Jun 1980 and a record low of 3.000 % in May 2014. United States CSI: Expected Interest Rates: Next Yr: Go Down data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The question was: No one can say for sure, but what do you think will happen to interest rates for borrowing money during the next 12 months -- will they go up, stay the same, or go down?

Monthly and long-term United States Interest Rate data: historical series and analyst forecasts curated by FocusEconomics.

The FHFA stress test is updated each quarter according to objective rules derived from fundamental economic relationships. These rules affect a dynamic adjustment to the severity of the stress test that accounts for current economic conditions, specifically the current level of house prices relative to the ongoing house price cycle. The stress test incorporates different house-price level (HPI) stress paths for each state, thus accounting for the fact that house price cycles can differ significantly from one state or region to another. The severity of the economic stress imposed by the test, as measured by the projected percentage drop in HPI, changes over time for each state corresponding to the deviation of current HPI from its long-run trend. As a result of this design, the FHFA stress test will produce countercyclical economic capital requirements, in that the estimates of potential losses on new mortgage loan originations increase during economic expansions, as current HPI rises above its long-term trend, and decrease during economic contractions, as current HPI falls to or below trend. The dynamic adjustment feature of the stress test allows that it will accommodate any size current house price cycle, even those of greater amplitude than any observed previously. Further, the severity of the stress test is calibrated to produce economic capital requirements that are sufficient, as of the day of origination, to fully capitalize the mortgage assets for the life of those assets.

Greece Lending Rate: Outstanding Amount (OA): Households: Mortgage Loans: Over 1 and Up to 5 Years data was reported at 4.556 % pa in Sep 2018. This records an increase from the previous number of 4.554 % pa for Aug 2018. Greece Lending Rate: Outstanding Amount (OA): Households: Mortgage Loans: Over 1 and Up to 5 Years data is updated monthly, averaging 4.574 % pa from Sep 2002 (Median) to Sep 2018, with 193 observations. The data reached an all-time high of 6.580 % pa in May 2003 and a record low of 3.353 % pa in Jul 2016. Greece Lending Rate: Outstanding Amount (OA): Households: Mortgage Loans: Over 1 and Up to 5 Years data remains active status in CEIC and is reported by Bank of Greece. The data is categorized under Global Database’s Greece – Table GR.M005: Lending Rates.

The data in this dataset is collected from FRED.

I decided to create this dataset while reading the research paper Factors Affecting House Prices in Cyprus: 1988-2008 by Panos Pashardes & Christos S. Savva. This research paper is extremely informative and covers a lot of details regarding the macroeconomics involved in real estate market. So I would recommend you all to go through it once.

NOTE:

This dataset will be updated over a period of time and include the following: - Macroeconomic factors with quarterly, monthly frequencies. - Microeconomic factors such as house type, age, location, size (BR, BA, carpet area/built-up area), facilities, view, disability functions, region, house prices, etc.

NOTE 2:

I recommend you all to check the file in this dataset with the title Housing_Macroeconomic_Factors_US (2).csv, it includes both the supply and demand factors associated with the housing market.

General Defintions:

1. Macroeconomic Factors

• House_Price_Index: House price change according to the index base period set (you can check the date at which this value is 100).
• Stock_Price_Index: Stock price change according to the index base period set (you can check the date at which this value is 100).
• Consumer_Price_Index: The Consumer Price Index measures the overall change in consumer prices based on a representative basket of goods and services over time.
• Population: Population of USA (unit: thousands).
• Unemployment_Rate: Unemployment rate of USA (unit: percentage).
• Real_GDP: GDP with adjusted inflation (Annual version unit: billions of chain 2012 dollars in, Monthly version unit: Annualised change).
• Mortgage_Rate: Interest charged on mortgages (unit: percentage).
• Real_Disposable_Income (Real Disposable Personal Income): Money left from salary after all the taxes are paid (unit: billions of chain 2012 dollars).
• Inflation: Decline in purchasing power over time (unit: percentage). [Forgot to remove this column in Annual version since CPI is one of the measures used to determine inflation].

What can you do with this dataset?

• Perform statistical analysis, find significant features & find the value by which these features affect the house price index (recommend to use a percentage change instead of index).
• Perform multivariate regression and predict the price of houses using microeconomic features (soon).

Thanks! If you like this dataset, I'll appreciate it if you give this dataset a vote! Discussions, suggestions & doubts are always welcome. Happy Learning!!

Reference Paper:

M. Bechini, M. Lavagna, P. Lunghi, Dataset generation and validation for spacecraft pose estimation via monocular images processing, Acta Astronautica 204 (2023) 358–369

M. Bechini, P. Lunghi, M. Lavagna. "Spacecraft Pose Estimation via Monocular Image Processing: Dataset Generation and Validation". In 9th European Conference for Aeronautics and Aerospace Sciences (EUCASS)

General Description:

The "Tango Spacecraft Dataset for Region of Interest Estimation and Semantic Segmentation" dataset here published should be used for Region of Interest (ROI) and/or semantic segmentation tasks. It is split into 30002 train images and 3002 test images representing the Tango spacecraft from Prisma mission, being the largest publicly available dataset of synthetic space-borne noise-free images tailored to ROI extraction and Semantic Segmentation tasks (up to our knowledge). The label of each image gives, for the Bounding Box annotations, the filename of the image, the ROI top-left corner (minimum x, minimum y) in pixels, the ROI bottom-right corner (maximum x, maximum y) in pixels, and the center point of the ROI in pixels. The annotation are taken in image reference frame with the origin located at the top-left corner of the image, positive x rightward and positive y downward. Concerning the Semantic Segmentation, RGB masks are provided. Each RGB mask correspond to a single image in both train and test dataset. The RGB images are such that the R channel corresponds to the spacecraft, the G channel corresponds to the Earth (if present), and the B channel corresponds to the background (deep space). Per each channel the pixels have non-zero value only in correspondence of the object that they represent (Tango, Earth, Deep Space). More information on the dataset split and on the label format are reported below.

Images Information:

The dataset comprises 30002 synthetic grayscale images of Tango spacecraft from Prisma mission that serves as train set, while the test set is formed by 3002 synthetic grayscale images of Tango spacecraft from Prisma mission in PNG format. About 1/6 of the images both in the train and in the test set have a non-black background, obtained by rendering an Earth-like model in the raytracing process used to define the images reported. The images are noise-free to increase the flexibility of the dataset. The illumination direction of the spacecraft in the scene is uniformly distributed in the 3D space in agreement with the Sun position constraints.

Labels Information:

Labels for the bounding box extraction are here provided in separated JSON files. The files are formatted per each image as in the following example:

filename  : tango_img_1       # name of the image to which the data are referred

rol_tl     : [x, y]              # ROI top-left corner (minimum x, minimum y) in pixels

roi_br     : [x, y]             # ROI bottom-right corner (maximum x, maximum y) in pixels

roi_cc     : [x, y]             # center point of the ROI in pixels

Notice that the annotation are taken in image reference frame with the origin located at the top-left corner of the image, positive x rightward and positive y downward.To make the usage of the dataset easier, both the training set and the test set are split in two folders containing the images with earth as background and without background.

Concerning the Semantic Segmentation Labels, they are provided as RGB masks named as "filename_mask.png" where "filename" is the filename of the image of the training set or the test set to which a specific mask is referred. The RGB images are such that the R channel corresponds to the spacecraft, the G channel corresponds to the Earth (if present), and the B channel corresponds to the background (deep space). Per each channel the pixels have non-zero value only in correspondence of the object that they represent (Tango, Earth, Deep Space).

VERSION CONTROL

v1.0: This version contains the dataset (both train and test) of full scale images with ROI annotations and RGB masks for Semantic Segmentation tasks. These images have width=height=1024 pixels. The position of tango with respect to the camera is randomly selected from a uniform distribution, but it is ensured the full visibility in all the images.

Note: this dataset contains the same images of the "Tango Spacecraft Wireframe Dataset Model for Line Segments Detection" v2.0 full-scale (DOI: https://doi.org/10.5281/zenodo.6372848) and also "Tango Spacecraft Dataset for Monocular Pose Estimation" v1.0 (DOI: https://doi.org/10.5281/zenodo.6499007) and they can be used together by combining the annotations of the relative pose and the ones of the reprojected wireframe model of Tango, with also the ones of the ROI. These three datasets give the most comprehensive dataset of space borne synthetic images ever published (up to our knowledge).

CSI: Expected Interest Rates: Next Yr: Don’t Know data was reported at 2.000 % in May 2018. This records an increase from the previous number of 1.000 % for Apr 2018. CSI: Expected Interest Rates: Next Yr: Don’t Know data is updated monthly, averaging 2.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 14.000 % in Feb 1978 and a record low of 0.000 % in Nov 2017. CSI: Expected Interest Rates: Next Yr: Don’t Know data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The question was: No one can say for sure, but what do you think will happen to interest rates for borrowing money during the next 12 months -- will they go up, stay the same, or go down?

This dataset was generated from a public earning's call (press release article). And used to generate examples of the way real humans would speak regarding the matters in the article, within real world scenarios. Here they are below:

Here are the linguistic variations for each of the queries in the dataset, based on the example article provided:

Here are five examples related to strong average loan growth in US Personal Banking (#5):

1. Mortgage Loans: An increase in demand for mortgage loans contributed to the strong average loan growth in US Personal Banking. Customers taking advantage of low interest rates led to a surge in mortgage applications and approvals.

2. Auto Loans: Robust consumer spending and increased car sales led to higher demand for auto loans, contributing to the strong loan growth in US Personal Banking. Customers seeking financing options for purchasing vehicles played a significant role in this growth.

3. Personal Loans: The availability of personal loans with favorable terms and competitive interest rates attracted borrowers, resulting in strong average loan growth in US Personal Banking. Customers availed personal loans for various purposes such as home improvements, debt consolidation, or financing other personal expenses.

4. Small Business Loans: US Personal Banking also witnessed strong loan growth due to increased lending to small businesses. As entrepreneurs and small business owners sought capital for expansion, equipment purchases, or working capital, the demand for small business loans rose, contributing to the growth.

5. Student Loans: The higher education sector continued to rely on student loans to finance tuition fees and related expenses. With the increasing cost of education, a rise in student loan applications and approvals contributed to the strong average loan growth in US Personal Banking.

General Queries Query: "What was the revenue for Personal Banking and Wealth Management (PBWM) in the last quarter?"

Variation 1: "What were the PBWM revenues in the previous quarter?" Variation 2: "Can you provide the revenue figure for PBWM in the last quarter?" Variation 3: "How much revenue did PBWM generate in the last quarter?" Variation 4: "What was the total revenue for PBWM in the most recent quarter?" Variation 5: "Could you tell me the revenue earned by PBWM in the last quarter?" Query: "What were the revenue figures for different divisions under US Personal Banking?"

Variation 1: "Can you provide the revenue breakdown for various divisions within US Personal Banking?" Variation 2: "What were the revenues generated by the different divisions in US Personal Banking?" Variation 3: "How did the revenue distribution look across different divisions in US Personal Banking?" Variation 4: "What were the individual revenue figures for each division within US Personal Banking?" Variation 5: "Could you give me a breakdown of the revenues for different divisions in US Personal Banking?" Query: "How did operating expenses change for PBWM?"

Variation 1: "What was the change in operating expenses for PBWM?" Variation 2: "Were there any fluctuations in the operating expenses of PBWM?" Variation 3: "How did the operating expenses for PBWM evolve over the specified period?" Variation 4: "Can you provide insights into the changes in operating expenses for PBWM?" Variation 5: "What was the percentage change in operating expenses for PBWM?" Query: "What factors contributed to the increase in PBWM's cost of credit?"

Variation 1: "What were the drivers behind the rise in PBWM's cost of credit?" Variation 2: "Which factors influenced the increase in PBWM's cost of credit?" Variation 3: "Can you identify the elements that led to the higher cost of credit for PBWM?" Variation 4: "What were the contributing factors to the cost of credit escalation in PBWM?" Variation 5: "What were the key reasons behind the growth in PBWM's cost of credit?" Query: "What led to the decrease in PBWM's net income?"

Variation 1: "What were the factors responsible for the decline in PBWM's net income?" Variation 2: "Can you identify the causes of the reduction in PBWM's net income?" Variation 3: "What influenced the decrease in net income for PBWM?" Variation 4: "Were there specific drivers that contributed to the decline in PBWM's net income?" Variation 5: "What were the primary reasons behind the decrease in PBWM's net income?" These linguistic variations provide different ways to ask the same questions, allowing for a more diverse and robust training dataset for the chatbot.

Here are the extracted entities from the provided article:

Account Line Entities:

Revenues Operating expenses Cost of credit Net income Business Line Entities:

Personal Banking and Wealth Management (PBWM) Branded Cards Retail Services Retail Banking Global Wealth Management Markets Banking Investment Banking Corporate Lending...

Rental Affordability Analysis Based on Median Income

Trends in Tier-Based Affordability Across the U.S

By Zillow Data [source]

About this dataset

This dataset contains rental affordability data for different regions in the US, giving valuable insights into regional rental markets. Renters can use this information to identify where their budget will go the farthest. The cities are organized by rent tier in order to analyze affordability trends within and between different housing stock types. Within each region, the data includes median household income, Zillow Rent Index (ZRI), and percent of income spent on rent.

The Zillow Home Value Forecast (ZHVF) is used to calculate future combined mortgage pay/rent payments in each region using current median home prices, actual outstanding debt amounts and 30-year fixed mortgage interest rates reported through partnership with TransUnion credit bureau. Zillow also provides a breakdown of cash vs financing purchases for buyers looking for an investment or cash option solution.

This dataset provides an effective tool for consumers who want to better understand how their budget fits into diverse rental markets across the US; from condominiums and co-ops, multifamily residences with five or more units, duplexes and triplexes - every renter can determine how their housing budget should be adjusted as they consider multiple living possibilities throughout the country based on real-time price data!

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How to use the dataset

Introduction

Getting Started

• First, you'll need to download the TieredAffordability_Rental.csv dataset from this Kaggle page onto your computer or device.

• After downloading the data set onto your device, open it with any CSV viewing software of your choice (ex: Excel). It will include columns for RegionName**RegionName** , homes type/housing stock (All Homes or Condo/Co-op) SizeRank , Rent tier tier , Date date , median household income income , Zillow Rent Index zri and PercentIncomeSpentOnRent percentage (what portion of monthly median house-hold goes toward monthly mortgage payment) .

• To begin analyzing rental prices across different regions using this dataset, look first at column four: SizeRank; which ranks each region based on size - smallest regions listed first and largest at last - so that you can compare a similar range of Regions when looking at affordability by home sizes larger than one unit multiplex dwellings.*Duples/Triplex*. Once there is an understanding of how all homes compare overall now it is time to consider home types Multifamily 5+ units according to rent tiers tier .

• Next, choose one or more region(s) for comparison based on their rank in SizeRank column –so that all information gathered about them reflects what portionof households fall into certain categories ; eg; All Homes / Small Home /Large Home / MultiPlex Dwelling and what tier does each size rank falls into eg.: Affordable/Slightly Expensive/ Moderately Expensive etc.. This will enable further abstraction from other elements like date vs inflation rate per month or periodical intervals set herein by Rate segmentation i e dates givenin ‘Date’Columns – making the task easier and more direct while analyzing renatalAffordibility Analysis Based On Median Income zri 00 zwi & PCISOR 00 PCIRO

Research Ideas

• Use the PercentIncomeSpentOnRent column to compare rental affordability between regions within a particular tier and determine optimal rent tiers for relocating families.
• Analyze how market conditions are affecting rental affordability over time by using the income, zri, and PercentageIncomeSpentOnRent columns.
• Identify trends in housing prices for different tiers over the years by comparing SizeRank data with Zillow Home Value Forecast (ZHVF) numbers across different regions in order to identify locations that may be headed up or down in terms of home values (and therefore rent levels)

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: TieredAffordability_Rental.csv | Column name | Description | |:-----------------------------|:-------------------------------------------------------------| | RegionName | The name of the region. (String) ...

The benchmark interest rate in Canada was last recorded at 2.25 percent. This dataset provides - Canada Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Monthly and long-term Argentina Interest Rate data: historical series and analyst forecasts curated by FocusEconomics.

Comprehensive proprietary research analyzing 312,367 assumable mortgage homes from 2023-2025 across all 50 states, including interest rates, savings analysis, state distribution, price ranges, and down payment requirements.

Established in 1977, the programmed habitat improvement operations (OPAH) have been the main tool for the rehabilitation of urban centres and rural towns for the past 30 years. Other tools have been created to respond to territorial, technical and social specificities: declination of OPAHs (rural, urban, degraded condominiums), General Interest Programmes (GIPs) and Thematic Social Programmes (PST).When the planned intervention in a given area, generally large — large agglomeration, extensive habitat basin, or even department, these territories do not have significant urban and social dysfunctions, justifying an overall project — is a particular problem to be dealt with, social or technical, OPAH is not an adequate tool, and should be preferred to it the procedure of the Programme of General Interest (PGI), regulated by Article R 327-1 of the Code de la construction et de l’habitation (CCH).The general interest programme (PIG) is an action programme initiated by local and regional authorities benefiting from an agreement for the delegation of stone aid. It aims to provide solutions to specific problems relating to the improvement of housing in housing units or buildings on different scales (agglomeration, housing basin, canton, country or even department). Thus, the scope of intervention can be the housing of students, young workers, the elderly or the disabled, the reduction of the number of vacant dwellings, the increase in the supply of social housing or the fight against diffuse unhealthiness. In addition, exceptional situations resulting from a disaster, whether natural or not, may be dealt with within the framework of a GIP. The duration of the GIP is free, at the discretion of the local authorities, taking into account the local context and issues: one year, 3 years or more if a contractual framework is defined in advance between the programme partners. The data does not contain the old IMPs that are otherwise archived.For the record: the public interest programme must be distinguished from the project of general interest, also known as the GIP, provided for by the Urban Planning Code.