In the United States, interest rates for all mortgage types started to increase in 2021. This was due to the Federal Reserve introducing a series of hikes in the federal funds rate to contain the rising inflation. In the fourth quarter of 2024, the 30-year fixed rate rose slightly, to **** percent. Despite the increase, the rate remained below the peak of **** percent in the same quarter a year ago. Why have U.S. home sales decreased? Cheaper mortgages normally encourage consumers to buy homes, while higher borrowing costs have the opposite effect. As interest rates increased in 2022, the number of existing homes sold plummeted. Soaring house prices over the past 10 years have further affected housing affordability. Between 2013 and 2023, the median price of an existing single-family home risen by about ** percent. On the other hand, the median weekly earnings have risen much slower. Comparing mortgage terms and rates Between 2008 and 2023, the average rate on a 15-year fixed-rate mortgage in the United States stood between **** and **** percent. Over the same period, a 30-year mortgage term averaged a fixed-rate of between **** and **** percent. Rates on 15-year loan terms are lower to encourage a quicker repayment, which helps to improve a homeowner’s equity.

Graph and download economic data for 30-Year Fixed Rate FHA Mortgage Index (OBMMIFHA30YF) from 2017-01-03 to 2025-08-28 about FHA, 30-year, mortgage, fixed, rate, indexes, and USA.

Dataset Overview

This dataset provides historical housing price indices for the United States, covering a span of 20 years from January 2000 onwards. The data includes housing price trends at the national level, as well as for major metropolitan areas such as San Francisco, Los Angeles, New York, and more. It is ideal for understanding how housing prices have evolved over time and exploring regional differences in the housing market.

Why This Dataset?

The U.S. housing market has experienced significant shifts over the last two decades, influenced by economic booms, recessions, and post-pandemic recovery. This dataset allows data enthusiasts, economists, and real estate professionals to analyze long-term trends, make forecasts, and derive insights into regional housing markets.

What’s Included?

Time Period: January 2000 to the latest available data (specific end date depends on the dataset). Frequency: Monthly data. Regions Covered: 20+ U.S. cities, states, and aggregates.

Columns Description

Each column represents the housing price index for a specific region or aggregate, starting with a date column:

Date: Represents the date of the housing price index measurement, recorded with a monthly frequency. U.S. National: The national-level housing price index for the United States. 20-City Composite: The aggregate housing price index for the top 20 metropolitan areas in the U.S. CA-San Francisco: The housing price index for San Francisco, California. CA-Los Angeles: The housing price index for Los Angeles, California. WA-Seattle: The housing price index for Seattle, Washington. NY-New York: The housing price index for New York City, New York. Additional Columns: The dataset includes more columns with housing price indices for various U.S. cities, which can be viewed in the full dataset preview.

Potential Use Cases

Time-Series Analysis: Investigate long-term trends and patterns in housing prices. Forecasting: Build predictive models to forecast future housing prices using historical data. Regional Comparisons: Analyze how housing prices have grown in different cities over time. Economic Insights: Correlate housing prices with economic factors like interest rates, GDP, and inflation.

Who Can Use This Dataset?

This dataset is perfect for:

Data scientists and machine learning practitioners looking to build forecasting models. Economists and policymakers analyzing housing market dynamics. Real estate investors and analysts studying regional trends in housing prices.

Example Questions to Explore

Which cities have experienced the highest housing price growth over the last 20 years? How do housing price trends in coastal cities (e.g., Los Angeles, Miami) compare to midwestern cities (e.g., Chicago, Detroit)? Can we predict future housing prices using time-series models like ARIMA or Prophet?

Graph and download economic data for 30-Year Fixed Rate Jumbo Mortgage Index (OBMMIJUMBO30YF) from 2017-01-03 to 2025-08-28 about jumbo, 30-year, mortgage, fixed, rate, indexes, and USA.

Car loan interest rates in the United States decreased since mid-2024. Thus, the period of rapidly rising interest rates, when they increased from 3.85 percent in December 2021 to 7.92 percent in June 2024, has come to an end. The Federal Reserve interest rate is one of the main causes of the interest rates of loans rising or falling. If inflation stays under control, the Federal Reserve will start cutting the interest rates, which would have the effect of the cost of car loans falling too. How many cars have financing in the United States? Car financing exists because not everyone who wants or needs a car can purchase it outright. A financial institution will then lend the money to the customer for purchasing the car, which must then be repaid with interest. Most new vehicles in the United States in 2024 were purchased using car loans. It is not as common to use car loans for purchasing used vehicles as for new ones, although over a third of used vehicles were purchased using loans. The car industry in the United States The car financing business is huge in the United States, due to the high sales of both new and used vehicles in the country. A lot of the United States is very car-centric, which means that, outside large cities, it can often be difficult to do their daily commutes through other transportation methods. In fact, only a small percentage of U.S. workers used public transport to go to work. That is one of the factors that has helped establish the importance of the automotive sector in North America. Nevertheless, there are still countries in Asia-Pacific, Africa, the Middle East, and Europe with higher car-ownership rates than the United States.

The foreclosure rate in the United States has experienced significant fluctuations over the past two decades, reaching its peak in 2010 at **** percent following the financial crisis. Since then, the rate has steadily declined, with a notable drop to **** percent in 2021 due to government interventions during the COVID-19 pandemic. In 2024, the rate stood slightly higher at **** percent but remained well below historical averages, indicating a relatively stable housing market. Impact of economic conditions on foreclosures The foreclosure rate is closely tied to broader economic trends and housing market conditions. During the aftermath of the 2008 financial crisis, the share of non-performing mortgage loans climbed significantly, with loans 90 to 180 days past due reaching *** percent. Since then, the share of seriously delinquent loans has dropped notably, demonstrating a substantial improvement in mortgage performance. Among other things, the improved mortgage performance has to do with changes in the mortgage approval process. Homebuyers are subject to much stricter lending standards, such as higher credit score requirements. These changes ensure that borrowers can meet their payment obligations and are at a lower risk of defaulting and losing their home. Challenges for potential homebuyers Despite the low foreclosure rates, potential homebuyers face significant challenges in the current market. Homebuyer sentiment worsened substantially in 2021 and remained low across all age groups through 2024, with the 45 to 64 age group expressing the most negative outlook. Factors contributing to this sentiment include high housing costs and various financial obligations. For instance, in 2023, ** percent of non-homeowners reported that student loan expenses hindered their ability to save for a down payment.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

CEV Eaton Vance California Municipal Income Trust Shares of Beneficial Interest

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Southern California Edison: A Steady Path to Floating Rates? (SCE-J)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Debt service ratios, interest and obligated principal payments on debt, and related statistics for households, Canada.

Description PREFERRED BANK (PFBC) Preferred Bank (“PFBC”) is a community bank located in California that provides banking service to small and mid-sized businesses (“SMEs”) in California, Texas and New York. PFBC’s initial customers were from the Chinese community of Southern California. PFBC also originates and services SBA and commercial real estate loans. PFBC operates out of its headquarters in Los Angeles, California and twelve locations in California, one in Houston and one in New York City. PFBC has organically grown in Texas and San Francisco. PFBC is one of the most efficient bank in the United States (26% Efficiency Ratio) due to its streamlined loan origination process. PFBC has grown EPS by almost 18% per year over the past five and 22% over the past ten years. This growth is driven by providing commercial and commercial real estate loans which have grown by 15% per year over the past ten years and 10% per year over the past five years. PFBC’s lending franchise and loan purchase generates an average loan yield of 7.1% and has organically grown loans by 10% per year over the past five years. The strong loan growth is comprised of criticized plus watch list loans of 1.6%, non-performing assets (“NPAs”) of 0.5% and a loan loss reserve to NPAs of 435%. PFBC finances its loans through non-interest bearing and interest bearing deposits generating a low cost of funds of 3.6%. The resulting net interest margin (NIM) is 4.1% and is sustainable as funding costs will decline with declining loan yields. PFBC’s largest shareholder is its management, which holds 8% of its common stock. Historically, PFBC has generated on average less than five percentage of its revenue from non-interest bearing or spread activities. From 2013 to 2023, PFBC realized operational leverage from its loan growth over a slower growing fixed cost base. PFBC was founded in 1991 in Los Angeles, California to provide banking services to the Chinese community in Southern California. Over time, PBFC serviced a larger customer base including non-Chinese customers in Southern California. PFBC’s growth from Southern California came about from organic growth (opening branches) in San Francisco (2013) and Houston (2023). Expansion in New York City (2015) came from the acquisition of a Chinese bank, UIB, located in Flushing, NY. From 2013 to 2023, PFBC’s book value plus dividends increased by 15% per year and EPS grew by 22% per year. From 2020 to 2024, MSBC repurchased shares at a rate of about 2.5% per year. A bank productivity measure is the efficiency ratio, non-interest expense divided by total revenues. A good benchmark for efficiency is a 50% efficiency ratio. The average efficiency ratio for commercial banks in Q2 2024 was 56%. PFBC’s efficiency ratio is 26% for the trailing three quarters ending Q3 2024. PFBC has generated on average returns on equity of 18% over the past five years. This has been an increase from an average of 13% in the previous five-year period. The average incremental return on equity over the past five years has been 27%, see the calculation below. The ability to generate these returns is the result of increased efficiency and expansion in existing and new markets. Loan growth has been robust with 12% per year growth from 2013 to 2018 to 18% per year growth from 2019 to 2023. Below is a return on incremental equity capital (“RoIEC”) analysis for PFBC: PFBC has three levers for earnings growth: 1) expansion into new markets; 2) increased efficiency; and 3) distributing excess cash by buying back shares. PFBC has economies of scale in the service markets it currently or historically competed in (Local real estate and business loans). They also have scale based upon the volume of the loans they originate; so as they grow, they should become more efficient. Los Angeles, San Francisco, Queens and Houston and Ethnic Chinese Loan Market Is Apple a good investment? Apple Cost of Equity Apple Cost of Debt How to Invest in OpenAI How to Invest in SpaceX PFBC competes in California’s Los Angeles and San Francisco, New York City’s and Houston’s banking markets. The table below illustrates the population, income and housing price growth over the past five and ten years in the five MSAs MSBC competes in: These are healthy growth rates for PFBC to provide loans into. Downside Protection PFBC’s risks include both operational leverage and financial leverage. Operational leverage is based upon the fixed vs. variable costs of the operations. There are economies of scale related to some functions such as loan processing and cross-selling of banking services. For banks the amount of non-interest income can provide downside protection especially if this revenue is recurring as is the case for UBAB. Over the past five years, about 5% of PFBC’s revenues were from non-interest income. PFBC’s balance sheet, as of September 30, 2024 is comprised of $805 million of cash, $405 million of securities...

Property-Plant-and-Equipment-Gross Time Series for Zions Bancorporation. Zions Bancorporation, National Association provides various banking products and related services primarily in the states of Arizona, California, Colorado, Idaho, Nevada, New Mexico, Oregon, Texas, Utah, Washington, and Wyoming. The company operates through Zions Bank, California Bank & Trust, Amegy Bank, National Bank of Arizona, Nevada State Bank, Vectra Bank Colorado, and The Commerce Bank of Washington segments. It offers commercial and small business banking services to small- and medium-sized businesses, such as commercial, industrial, and owner-occupied lending and leasing; municipal and public finance services; depository account and cash management services; commercial and small business cards; merchant processing services; corporate trust services; and correspondent banking and international lending services. The company also provides capital markets and investment banking services, including loan syndications, foreign exchange services, interest rate derivatives, fixed income securities underwriting, advisory and capital raising, commercial mortgage-backed security conduit lending, and power and project financing; and commercial real estate lending services consisting of term and construction/land development financing for commercial and residential purposes. In addition, it offers retail banking services comprising residential mortgages, home equity lines of credit, personal lines of credit, installment consumer loans, depository account services, consumer cards, and personal trust services; and wealth management services consisting of investment management, fiduciary and estate, and advanced business succession and estate planning services. The company was formerly known as ZB, National Association and changed its name to Zions Bancorporation, National Association in September 2018. Zions Bancorporation, National Association was founded in 1873 and is headquartered in Salt Lake City, Utah.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

SCE^K Southern California Edison Company 5.45% Fixed-to-Floating Rate Trust Preference Securities

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data