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Description:

A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, nearness to mainroad, etc. The dataset is small yet, it's complexity arises due to the fact that it has strong multicollinearity. Can you overcome these obstacles & build a decent predictive model?

Acknowledgement:

Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102. Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple feature.
• Also evaluate the models & compare thier respective scores like R2, RMSE, etc.

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.

House Price Index YoY in the United States decreased to 1.70 percent in September from 2.40 percent in August of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

Context

The Federal Reserve sets interest rates to promote conditions that achieve the mandate set by the Congress — high employment, low and stable inflation, sustainable economic growth, and moderate long-term interest rates. Interest rates set by the Fed directly influence the cost of borrowing money. Lower interest rates encourage more people to obtain a mortgage for a new home or to borrow money for an automobile or for home improvement. Lower rates encourage businesses to borrow funds to invest in expansion such as purchasing new equipment, updating plants, or hiring more workers. Higher interest rates restrain such borrowing by consumers and businesses.

Content

This dataset includes data on the economic conditions in the United States on a monthly basis since 1954. The federal funds rate is the interest rate at which depository institutions trade federal funds (balances held at Federal Reserve Banks) with each other overnight. The rate that the borrowing institution pays to the lending institution is determined between the two banks; the weighted average rate for all of these types of negotiations is called the effective federal funds rate. The effective federal funds rate is determined by the market but is influenced by the Federal Reserve through open market operations to reach the federal funds rate target. The Federal Open Market Committee (FOMC) meets eight times a year to determine the federal funds target rate; the target rate transitioned to a target range with an upper and lower limit in December 2008. The real gross domestic product is calculated as the seasonally adjusted quarterly rate of change in the gross domestic product based on chained 2009 dollars. The unemployment rate represents the number of unemployed as a seasonally adjusted percentage of the labor force. The inflation rate reflects the monthly change in the Consumer Price Index of products excluding food and energy.

Acknowledgements

The interest rate data was published by the Federal Reserve Bank of St. Louis' economic data portal. The gross domestic product data was provided by the US Bureau of Economic Analysis; the unemployment and consumer price index data was provided by the US Bureau of Labor Statistics.

Inspiration

How does economic growth, unemployment, and inflation impact the Federal Reserve's interest rates decisions? How has the interest rate policy changed over time? Can you predict the Federal Reserve's next decision? Will the target range set in March 2017 be increased, decreased, or remain the same?

Art Presence & Property Prices in London

Quantifying the Relationship with Online Data

By [source]

About this dataset

This dataset explores the potential relationship between art presence and property prices in London neighborhoods. We conducted an analysis to investigate this by measuring the proportion of Flickr photographs with the keyword ‘art’ attached. We then compared that data to residential property price gains for each Inner London neighborhood, seeking out any associations or correlations between art presence and housing value. Our findings demonstrate the impact of aesthetics on neighborhoods, illustrating how visual environment influences socio-economic conditions. With this dataset, we aim to show how online platforms can be leveraged for quantitative data collection and analysis which can visualize these relationships so as to better understand our urban settings

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How to use the dataset

This dataset can be used to investigate the relationship between art presence and property prices in London neighborhoods. The dataset includes three columns – Postcode.District, Rank.Mean.Change, and Proportion.Art.Photos – which provide quantitative analyses of the association between art presence and price gains for London neighborhoods.

To use this dataset, first identify the postcode district for which you wish to access data by referencing a street list or PostCodeSearcher website that outlines postcodes for each neighborhood in London(http://postcodesearcher.com/london). This will allow you to easily find properties within each neighborhood as there are specific postcode districts that demarcate boundaries of particular areas (for example W2 covers Bayswater).

Once you have identified a postcode district of interest, review the ‘Rank.Mean Change’ column to explore how residential property prices have changed relative to other areas in Inner London since 2010-13 using fractions (1 = highest gain; 25 = lowest gain). Focusing on one particular location will also provide an idea about their current pricing level compared with others in order to evaluate whether further investment is worthwhile or not based on its past history of growth rates . It is important to note that higher rank numbers indicate higher price gains while lower rank numbers indicate lower price gains relative with respect from 2010-13 timeframe therefore comparing these values across many neighborhoods gives an indication as what area offers more value growth wise over given time period..

Finally pay attention how much did art contributes as far change in property price goes? To answer this question , review ‘Proportion Art Photos’ column which provides ratio of Flickr photographs associated with keyword 'art' attached within given regions helps identify visual characteristics within different localities.. Comparing proportions across various locations provide detail information regarding how much did share visual aesthetic characterstics impacts change in pricings accross different region.. For example it can give us further understandings if majority photographs are made up of urban landscape , abstracts or simply portrait presences had any role play when we look at relativity gains over past few years? Such comparisons help inform our understanding about potential impact art presence can have on changes stay relatively stable even during volatile market times..

By combining this data with other datasets related to demographics, infrastructure and socioeconomics present within londons different areas we can gain further insight which then allows us making informed decisions when it comes investing particular locations .

Research Ideas

• Use this dataset to develop a predictive analytics model to identify areas in London most likely to experience an increase in residential property prices associated with the presence of art.
• Use this dataset to develop strategies and policies that promote both artistic expression and urban development in Inner London neighborhoods.
• Compare the presence of art across inner London boroughs, as well as against other cities, to gain insight into the socio-economic conditions related to the visual environment of a city and its impact on life quality for citizens

Acknowledgements

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

License

**License: [CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication](https://creativecommons.org/publicd...

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.

The interest rate set by the Federal Reserve is a crucial tool for promoting economic conditions that meet the mandate established by the United States Congress, which includes high employment, low and stable inflation, sustainable economic growth, and the moderation of long-term interest rates. The interest rates determined by the Fed directly influence the cost of credit, making financing either more accessible or more restrictive. When interest rates are low, there is a greater incentive for consumers to purchase homes through mortgages, finance automobiles, or undertake home renovations. Additionally, businesses are encouraged to invest in expanding their operations, whether by purchasing new equipment, modernizing facilities, or hiring more workers. Conversely, higher interest rates tend to curb such activity, discouraging borrowing and slowing economic expansion.

The dataset analyzed contains information on the economic conditions in the United States on a monthly basis since 1954, including the federal funds rate, which represents the percentage at which financial institutions trade reserves held at the Federal Reserve with each other in the interbank market overnight. This rate is determined by the market but is directly influenced by the Federal Reserve through open market operations to reach the established target. The Federal Open Market Committee (FOMC) meets eight times a year to determine the federal funds rate target, which has been defined within a range with upper and lower limits since December 2008.

Furthermore, real Gross Domestic Product (GDP) is calculated based on the seasonally adjusted quarterly rate of change in the economy, using chained 2009 dollars as a reference. The unemployment rate represents the seasonally adjusted percentage of the labor force that is unemployed. Meanwhile, the inflation rate is determined by the monthly change in the Consumer Price Index, excluding food and energy prices for a more stable analysis of core inflation.

The interest rate data was sourced from the Federal Reserve Bank of St. Louis' economic data portal, while GDP information was provided by the U.S. Bureau of Economic Analysis, and unemployment and inflation data were made available by the U.S. Bureau of Labor Statistics.

The analysis of this data helps to understand how economic growth, the unemployment rate, and inflation influence the Federal Reserve’s monetary policy decisions. Additionally, it allows for a study of the evolution of interest rate policies over time and raises the question of how predictable the Fed’s future decisions may be. Based on observed trends, it is possible to speculate whether the target range set in March 2017 will be maintained, lowered, or increased, considering the prevailing economic context and the challenges faced in conducting U.S. monetary policy.

The index relates to costs ruling on the first day of each month. NATIONAL HOUSE CONSTRUCTION COST INDEX; Up until October 2006 it was known as the National House Building Index Oct 2000 data; The index since October, 2000, includes the first phase of an agreement following a review of rates of pay and grading structures for the Construction Industry and the first phase increase under the PPF. April, May and June 2001; Figures revised in July 2001due to 2% PPF Revised Terms. March 2002; The drop in the March 2002 figure is due to a decrease in the rate of PRSI from 12% to 10¾% with effect from 1 March 2002. The index from April 2002 excludes the one-off lump sum payment equal to 1% of basic pay on 1 April 2002 under the PPF. April, May, June 2003; Figures revised in August'03 due to the backdated increase of 3% from 1April 2003 under the National Partnership Agreement 'Sustaining Progress'. The increases in April and October 2006 index are due to Social Partnership Agreement "Towards 2016". March 2011; The drop in the March 2011 figure is due to a 7.5% decrease in labour costs. Methodology in producing the Index Prior to October 2006: The index relates solely to labour and material costs which should normally not exceed 65% of the total price of a house. It does not include items such as overheads, profit, interest charges, land development etc. The House Building Cost Index monitors labour costs in the construction industry and the cost of building materials. It does not include items such as overheads, profit, interest charges or land development. The labour costs include insurance cover and the building material costs include V.A.T. Coverage: The type of construction covered is a typical 3 bed-roomed, 2 level local authority house and the index is applied on a national basis. Data Collection: The labour costs are based on agreed labour rates, allowances etc. The building material prices are collected at the beginning of each month from the same suppliers for the same representative basket. Calculation: Labour and material costs for the construction of a typical 3 bed-roomed house are weighted together to produce the index. Post October 2006: The name change from the House Building Cost Index to the House Construction Cost Index was introduced in October 2006 when the method of assessing the materials sub-index was changed from pricing a basket of materials (representative of a typical 2 storey 3 bedroomed local authority house) to the CSO Table 3 Wholesale Price Index. The new Index does maintains continuity with the old HBCI. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Oct 2008 data; Decrease due to a fall in the Oct Wholesale Price Index. .hidden { display: none }

The Chinese economy has undergone a long-term transition reform, but there is still a planned economy characteristic in the financial sector, which is financial repression. Due to the existence of financial repression, China’s actual interest rate level should be lower than the Consumer Price Index (CPI). However, based on official China’s interest rates and CPI, over half of the years China’s actual interest rate remained higher than CPI by our calculation from 1999 to 2022. This is inconsistent with the financial repression that exists in China, and the main reason is the calculation methods of China’s CPI. China’s CPI measurement system originated from the planned economy era, which did not fully consider the rise in housing purchase prices, so the current CPI measurement system can be more realistically presented by taking the rise in housing prices into consider. The core idea of this study is to mining relevant official statistical data and calculate the proportion of Chinese residents’ expenditure on purchasing houses to their total expenditure. By taking the proportion of house purchases as the weight of house price factor, and taking the proportion of other consumption as the weight of official CPI, the Generalized CPI (GCPI) is formulated. The GCPI is then compared with market interest rates to determine the actual interest rate situation in China over the past 20 years. This study has found that if GCPI is used as a measure, China’s real interest rates have been negative for most years since 1999. Chinese residents have suffered the negative effects of financial repression over the past 20 years, and their property income cannot keep up with the actual losses caused by inflation. Therefore, it is believed that China’s CPI calculation method should be adjusted to take into account the rise in housing prices, so China’s actual inflation level could be more accurately reflected. In view of the above, deepening interest rate marketization reform and expand channels for financial investment are the future development goals of China’s financial system.

Operating-Income Time Series for Japan Real Estate Investment Corp. Japan Real Estate Investment Corporation (the "Company") was established on May 11, 2001 pursuant to Japan's Act on Investment Trusts and Investment Corporations ("ITA"). The Company was listed on the real estate investment trust market of the Tokyo Stock Exchange ("TSE") on September 10, 2001 (Securities Code: 8952). Since its IPO, the size of the Company's assets (total acquisition price) has grown steadily, expanding from 92.8 billion yen to 1,167.7 billion yen as of March 31, 2025. Over the same period, the Company's portfolio has also increased from 20 properties to 77 properties. During the March 2025 period (October 1, 2024 to March 31, 2025), the Japanese economy continued to demonstrate a gradual recovery, despite some lingering stagnation in capital investment and personal consumption due to inflation and other factors. On the other hand, given the policy rate hikes by the Bank of Japan, the shift in global interest rates to a lowering phase, the impact of U.S. policy trends, such as trade policy and other factors, interest rate trends, overseas political and economic developments, and price trends, including resource prices, will continue to bear watching. In the office leasing market, demand continues to grow for leases driven by business expansion and relocations aimed at improving location. As a result, the vacancy rate in central Tokyo continues to decline gradually. In addition, rent levels are rising at an accelerating rate. In light of the prevailing conditions in the leasing market, the Company is striving to attract new tenants through strategic leasing activities and to further enhance the satisfaction level of existing tenants by adding value to its portfolio properties with the aim of maintaining and improving the occupancy rate and realizing sustainable income growth across the entire portfolio. In the real estate trading market, despite the Bank of Japan normalizing its monetary policy, the appetite for property acquisition among both domestic and foreign investors remains firm, backed ma

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.

Begin-Period-Cashflow Time Series for Japan Real Estate Investment Corp. Japan Real Estate Investment Corporation (the "Company") was established on May 11, 2001 pursuant to Japan's Act on Investment Trusts and Investment Corporations ("ITA"). The Company was listed on the real estate investment trust market of the Tokyo Stock Exchange ("TSE") on September 10, 2001 (Securities Code: 8952). Since its IPO, the size of the Company's assets (total acquisition price) has grown steadily, expanding from 92.8 billion yen to 1,167.7 billion yen as of March 31, 2025. Over the same period, the Company's portfolio has also increased from 20 properties to 77 properties. During the March 2025 period (October 1, 2024 to March 31, 2025), the Japanese economy continued to demonstrate a gradual recovery, despite some lingering stagnation in capital investment and personal consumption due to inflation and other factors. On the other hand, given the policy rate hikes by the Bank of Japan, the shift in global interest rates to a lowering phase, the impact of U.S. policy trends, such as trade policy and other factors, interest rate trends, overseas political and economic developments, and price trends, including resource prices, will continue to bear watching. In the office leasing market, demand continues to grow for leases driven by business expansion and relocations aimed at improving location. As a result, the vacancy rate in central Tokyo continues to decline gradually. In addition, rent levels are rising at an accelerating rate. In light of the prevailing conditions in the leasing market, the Company is striving to attract new tenants through strategic leasing activities and to further enhance the satisfaction level of existing tenants by adding value to its portfolio properties with the aim of maintaining and improving the occupancy rate and realizing sustainable income growth across the entire portfolio. In the real estate trading market, despite the Bank of Japan normalizing its monetary policy, the appetite for property acquisition among both domestic and foreign investors remains firm, backed ma

Abstract This paper presents the more commonly used methods of hedonic estimation and applies them to a fiscal data base from Belo Horizonte, Brazil, 1995-2012. The results of the price indexes from the various estimations suggest a pronounced real estate appreciation from 2005 to 2012. Increasing real estate financing (after the statutory lien legislation of 2004), falling interest rates and growth in real family income contributed to the positive performance of the real estate market in the period. Results also indicate that real estate prices continued to increase despite the world financial crisis of 2008/2009 possibly due to countercyclical policies implemented by the Brazilian Government. These results help to shed light on the potential use of hedonic price models and a fiscal database to construct an official housing price index for Brazil in the future.

BUSINESS PROBLEM-1 BACKGROUND: The Lending Club is a peer-to-peer lending site where members make loans to each other. The site makes anonymized data on loans and borrowers publicly available. BUSINESS PROBLEM: Using lending club loans data, the team would like to test below hypothesis on how different factors effecing each other (Hint: You may leverage hypothesis testing using statistical tests) a. Intrest rate is varied for different loan amounts (Less intrest charged for high loan amounts) b. Loan length is directly effecting intrest rate. c. Inrest rate varies for different purpose of loans d. There is relationship between FICO scores and Home Ownership. It means that, People with owning home will have high FICO scores. DATA AVAILABLE:  LoansData.csv The data have the following variables (with data type and explanation of meaning)  Amount.Requested - numeric. The amount (in dollars) requested in the loan application.  Amount.Funded.By.Investors - numeric. The amount (in dollars) loaned to the individual.  Interest.rate – character. The lending interest rate charged to the borrower.  Loan.length - character. The length of time (in months) of the loan.  Loan.Purpose – categorical variable. The purpose of the loan as stated by the applicant.  Debt.to.Income.Ratio – character. The % of consumer’s gross income going toward paying debts.  State - character. The abbreviation for the U.S. state of residence of the loan applicant.  Home.ownership - character. Indicates whether the applicant owns, rents, or has a mortgage.  Monthly.income - categorical. The monthly income of the applicant (in dollars).  FICO.range – categorical (expressed as a string label e.g. “650-655”). A range indicating the applicants FICO score.  Open.CREDIT.Lines - numeric. The number of open lines of credit at the time of application.  Revolving.CREDIT.Balance - numeric. The total amount outstanding all lines of credit.  Inquiries.in.the.Last.6.Months - numeric. Number of credit inquiries in the previous 6 months.  Employment.Length - character. Length of time employed at current job.

BUSINESS PROBLEM - 2 BACKGROUND: When an order is placed by a customer of a small manufacturing company, a price quote must be developed for that order. Because each order is unique, quotes must be established on an order-by-order basis by a pricing expert. The price quote process is laborintensive, as prices depend on many factors such as the part number, customer, geographic location, market, and order volume. The sales department manager is concerned that the pricing process is too complex, and that there might be too much variability in the quoted prices. An improvement team is tasked with studying and improving the pricing process. After interviewing experts to develop a better understanding of the current process, the team designed a study to determine if there is variability between pricing experts. That is, do different pricing experts provide different price quotes? Two randomly selected pricing experts, Mary and Barry, were asked to independently provide prices for twelve randomly selected orders. Each expert provided one price for each of the twelve orders. BUSINESS PROBLEM: We would like to assess if there is any difference in the average price quotes provided by Mary and Barry. DATA AVAILABLE:  Price_Quotes.csv The data set contains the order number, 1 through 12, and the price quotes by Mary and Barry for each order. Each row in the data set is the same order. Thus, Mary and Barry produced quotes for the same orders. BUSINESS PROBLEM-3: BACKGROUND: The New Life Residential Treatment Facility is a NGO that treatsteenagers who have shown signs of mental illness. It provides housing and supervision of teenagers who are making the transition from psychiatric hospitals back into the community. Because many of the teenagers were severely abused as children and have been involved with the juvenile justice system, behavioral problems are common at New Life. Employee pay is low and staff turnover (attrition) is high. A reengineering program wasinstituted at New Life with the goals of lowering behavioral problems of the kids and decreasing employee turnover rates. As a part of this effort, the following changes were made:  Employee shifts were shortened from 10 hours to 8 hours each day.  Employees were motivated to become more involved in patient treatments. This included encouraging staff to run varioustherapeutic treatment sessions and allowing staff to have more say in program changes.  The activities budget wasincreased.  A facility-wide performance evaluation system was putinto place that rewarded staff participation andinnovation.  Management and staff instituted a program designed to raise expectations about appropriate behavior from the kids. Thisincluded strict compliance with reporting of behavioral violations, insistence o...

Pretax-Margin Time Series for Japan Real Estate Investment Corp. Japan Real Estate Investment Corporation (the "Company") was established on May 11, 2001 pursuant to Japan's Act on Investment Trusts and Investment Corporations ("ITA"). The Company was listed on the real estate investment trust market of the Tokyo Stock Exchange ("TSE") on September 10, 2001 (Securities Code: 8952). Since its IPO, the size of the Company's assets (total acquisition price) has grown steadily, expanding from 92.8 billion yen to 1,167.7 billion yen as of March 31, 2025. Over the same period, the Company's portfolio has also increased from 20 properties to 77 properties. During the March 2025 period (October 1, 2024 to March 31, 2025), the Japanese economy continued to demonstrate a gradual recovery, despite some lingering stagnation in capital investment and personal consumption due to inflation and other factors. On the other hand, given the policy rate hikes by the Bank of Japan, the shift in global interest rates to a lowering phase, the impact of U.S. policy trends, such as trade policy and other factors, interest rate trends, overseas political and economic developments, and price trends, including resource prices, will continue to bear watching. In the office leasing market, demand continues to grow for leases driven by business expansion and relocations aimed at improving location. As a result, the vacancy rate in central Tokyo continues to decline gradually. In addition, rent levels are rising at an accelerating rate. In light of the prevailing conditions in the leasing market, the Company is striving to attract new tenants through strategic leasing activities and to further enhance the satisfaction level of existing tenants by adding value to its portfolio properties with the aim of maintaining and improving the occupancy rate and realizing sustainable income growth across the entire portfolio. In the real estate trading market, despite the Bank of Japan normalizing its monetary policy, the appetite for property acquisition among both domestic and foreign investors remains firm, backed ma

Average House Prices in Canada increased to 688800 CAD in October from 687600 CAD in September of 2025. This dataset includes a chart with historical data for Canada Average House Prices.

The benchmark interest rate in Norway was last recorded at 4 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.

The benchmark interest rate in China was last recorded at 3 percent. This dataset provides the latest reported value for - China Interest 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 Mexico was last recorded at 7.25 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Germany was last recorded at 4.50 percent. This dataset provides - Germany Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.