Graph and download economic data for 30-Year Fixed Rate USDA Mortgage Index (OBMMIUSDA30YF) from 2017-01-03 to 2025-07-21 about USDA, 30-year, fixed, mortgage, rate, indexes, and USA.

Mortgage interest rates in Spain soared in 2022, after falling below 1.5 percent at the end of 2021. In the second quarter of 2024, the average weighted interest rate stood at 3.46 percent. That was lower than the rate in the same period the previous year. Despite the increase, Spain had a considerably lower mortgage interest rate than many other European countries.The aftermath of the property bubble Before the bursting of the real estate bubble, the housing market experienced a period of intense activity. A context marked by economic growth, high employment rate, low interest rates, skyrocketing house prices and land speculation, among others, encourage massive lending for the acquisition of property; in 2005 alone, more than 1.3 million home mortgages were granted in Spain. When the bubble burst and the financial crisis hit the country, residential real estate transactions plummeted and households’ non-performing loans jumped to nearly 50 billion euros as countless families were not able to cope with their debts. Over a decade after the onset of the crisis, and despite falling mortgage rates, the volume of mortgage loans keeps decreasing every year. A homeowner country Traditionally, Spain has been a country of homeowners; in 2021, the homeownership rate was roughly 76 percent. While nearly half of Spanish households own their property with no outstanding payment, the percentage of households that have loan or mortgage pending has been decreasing in recent years. Despite ownership remaining as the preferred tenure option, cultural changes, job insecurity and mounting house prices are prompting Spaniards to opt more and more to become tenants instead of owners, as shown in the changing dynamics of the Spanish residential rental market.

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Agricultural Equipment Finance Market Size 2024-2028

The agricultural equipment finance market size is forecast to increase by USD 182.8 billion at a CAGR of 6% between 2023 and 2028.

The market is experiencing significant growth due to several key factors. Firstly, the easy accessibility to credit is driving the market, enabling farmers to invest in new and advanced agricultural equipment. Secondly, the replacement of outdated machinery with modern and efficient technology is a major trend for farm equipment, as farmers seek to increase productivity and reduce operational costs. However, the market is not without challenges. The turbulent economic and political environment poses risks, including inflation, interest rate fluctuations, and regulatory changes. These factors can impact the affordability and availability of credit, potentially hindering market growth. Despite these challenges, the market is expected to continue expanding as farmers prioritize investment in technology to enhance their operations and remain competitive.

What will be the Size of the Agricultural Equipment Finance Market During the Forecast Period?

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The market is a vital segment of the farm mechanization industry, facilitating the acquisition of essential machinery and utility vehicles for agricultural enterprises. This market is experiencing significant growth, driven by the increasing demand for credit to finance the purchase of advanced agricultural machinery, including tractors, combines, harvesters, planters, and utility vehicles. Finance companies play a crucial role in providing loans for farm equipment, offering both secured and unsecured options. Online financial platforms are transforming the agricultural equipment finance landscape, providing quick loan approvals and real-time information transparency. Blockchain technology is also gaining traction, offering secure, decentralized transactions and increased efficiency.
Alternative finance options, such as farm loan waivers and agricultural enterprise financing, are increasingly popular among farmers. Agricultural productivity growth, driven by precision agriculture and contract farming, is further fueling demand for agricultural equipment finance. Innovations like drones and advanced machinery are revolutionizing large-scale farming operations, necessitating significant investments. The Farm Service Agency (FSA) and other financial institutions continue to support farmers through various loan programs and services. Overall, the market is a dynamic and evolving sector, adapting to the ever-changing needs of the agricultural industry.

How is this Agricultural Equipment Finance Industry segmented and which is the largest segment?

The agricultural equipment finance industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Lease
  Loan
  Line of credit


Product

  Tractors
  Harvesters
  Haying equipment
  Others


Geography

  APAC

    China
    India


  North America

    US


  Europe

    Germany
    UK


  South America



  Middle East and Africa

By Type Insights

The lease segment is estimated to witness significant growth during the forecast period.

The market is characterized by various financing options, including leases, unsecured loans, and specialized loan programs. Leases accounted for the largest market share in 2023 due to their flexibility and convenience. Financing companies offer leases based on the residual fair market value, allowing borrowers to make payments at the end of the lease term if they wish to own the equipment. Alternatively, they can return the equipment to the company. Rental equipment usage is increasing in North America, Western Europe, and APAC due to its cost-effectiveness and ease of access. Additionally, alternative finance platforms, online financial platforms, and finance companies offer quick loan approvals and farm loan waivers to cater to the growing credit demand.

Farmers can acquire a range of agricultural machinery, from utility vehicles and tractors to combines, harvesters, planters, and irrigation equipment, through financing programs. The integration of blockchain technology, fintech, and data analytics in agricultural finance enhances credit scoring and loan processing efficiency. Eco-friendly equipment, precision agriculture, and automated systems are gaining popularity, driving the demand for agricultural equipment financing. Farm size, location, and borrower creditworthiness influence financing options, including loans, leases, lines of credit, and crop loans. Key players include financial institutions, agricultural productiveness research firms, and market research publishers such as Increasery, Increasons, and

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Haying Machinery Market Size 2024-2028

The haying machinery market size is forecast to increase by USD 3.33 billion at a CAGR of 5.91% between 2023 and 2028. The market is experiencing significant growth, driven by various factors. Government initiatives to support the mechanization of agricultural operations, particularly in developing countries, are a major catalyst. Integration of advanced technology, such as GPS guidance systems and autonomous steering, is another key trend, enhancing efficiency and productivity.

However, volatility in raw material prices poses a challenge, as does the high initial investment required for purchasing modern haying machinery. Despite these challenges, the market is expected to continue expanding due to the increasing demand for mechanized agricultural processes and the need to improve farming productivity.

Market Analysis

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The market is a significant segment of the agricultural equipment industry, encompassing various types of machinery used in the production, processing, and storage of hay. Key players in this market include balers, forage harvesters, disc mowers, mower conditioners, rakes, tedders, blowers, storage boxes, and moisture sensors. Grain farmers rely on hay as a crucial source of feed for livestock during the winter months. The market is driven by factors such as the need for self-sufficiency in animal feed production, increasing demand for plant-based feed, and the availability of credit facilities, farm subsidies, and low-interest rates. Automation is a significant trend in the market, with self-propelled balers and self-propelled forage harvesters gaining popularity due to their maneuverability and precision agriculture capabilities.

Other factors influencing market growth include the drying process, OEMs, aftermarkets, and large farms. The market for haying machinery includes various types of machinery used in the production, processing, and storage of hay, including balers, forage harvesters, disc mowers, mower conditioners, rakes, tedders, blowers, storage boxes, and moisture sensors. These machines are essential for farmers to ensure a consistent and efficient haying process, from the cutting and drying of the crop to the baling and storage of the final product. The market is expected to grow significantly in the coming years, driven by the increasing demand for plant-based feed and the need for self-sufficiency in animal feed production.

Additionally, the availability of credit facilities, farm subsidies, and low-interest rates is making it easier for farmers to invest in new and more efficient machinery. Self-propelled balers and self-propelled forage harvesters are gaining popularity in the market due to their maneuverability and precision agriculture capabilities. These machines offer farmers the ability to efficiently harvest and process large quantities of hay in a short amount of time, making them an essential investment for large-scale farming operations. Automation is another trend that is influencing the market. The integration of technology into haying machinery is making it possible for farmers to monitor and control their equipment remotely, ensuring optimal performance and reducing the need for manual labor.

The market is also being driven by the drying process, which is a critical step in the production of high-quality hay. Moisture sensors are becoming increasingly common in haying machinery, allowing farmers to monitor the moisture content of their hay in real-time and ensure that it is dried to the optimal level. OEMs and aftermarkets are also playing a significant role in the market. OEMs are constantly innovating and developing new machinery to meet the evolving needs of farmers, while aftermarkets offer farmers the ability to repair and maintain their existing machinery, ensuring that it remains in good working order.

In conclusion, the market is a dynamic and growing industry that plays a crucial role in the production of hay for animal feed. Driven by factors such as the increasing demand for plant-based feed, the need for self-sufficiency in animal feed production, and the availability of credit facilities, farm subsidies, and low-interest rates, the market is expected to continue growing in the coming years. With trends such as automation, precision agriculture, and the integration of technology into haying machinery, farmers are able to produce higher quality hay more efficiently and effectively than ever before.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Large farm
  Small and medium farm


Product

  Balers
  Mowers
  Tedders and rakes


Geography

  Europe

    Germany
    France
    Italy


  North America

Abstract

The Owner Transfer and Mortgage data covers over 450 million properties, and includes over 50 years of sales history. The tables were generated in June 2024, and cover all U.S. states, the U.S. Virgin Islands, Guam, and Washington, D.C.

The Owner Transfer data provides historical information about property sales and ownership-related transactions, including full, nominal, and quitclaim transactions (involving a change in title/ownership). It contains comprehensive property and transaction information, such as property characteristics, current ownership, transaction history, title company, cash purchase/foreclosure/resale/short sale indicators, and buyer information.

The Mortgage data provides historical information at the mortgage level, including purchase, refinance, equity, as well as details associated with each transaction, such as lender, loan amount, loan date, interest rate, etc. Mortgage details include mortgage amount, type of loan (conventional, FHA, VHA), mortgage rate type, mortgage purpose (cash out first, consolidation, standalone subordinate), mortgage ARM features, and mortgage indicators such as fixed-rate, conforming loan, construction loan, and private party. The Mortgage data also includes subordinate mortgage types, rate details, and lender details (NMLS ID, Loan Company, Loan Officers).

The CoreLogic Smart Data Platform (SDP) Owner Transfer and Mortgage data was formerly known as the CoreLogic Deed data. The CoreLogic Deed data contained both owner transfer and mortgage information. In the CoreLogic Smart Data Platform (SDP), this data was separated into two tables: Owner Transfer and Mortgage. Between the two tables, the CoreLogic Smart Data Platform (SDP) Owner Transfer and Mortgage data contains almost all of the variables that were included in the CoreLogic Deed data. Further, each CoreLogic Smart Data Platform (SDP) table is augmented with additional owner transfer and mortgage characteristics.

Methodology

In the United States, parcel data is public record information that describes a division of land (also referred to as "property" or "real estate"). Each parcel is given a unique identifier called an Assessor’s Parcel Number or APN. The two principal types of records maintained by county government agencies for each parcel of land are deed and property tax records. When a real estate transaction takes place (e.g. a change in ownership), a property deed must be signed by both the buyer and seller. The deed will then be filed with the County Recorder’s offices, sometimes called the County Clerk-Recorder or other similar title. Property tax records are maintained by County Tax Assessor’s offices; they show the amount of taxes assessed on a parcel and include a detailed description of any structures or buildings on the parcel, including year built, square footages, building type, amenities like a pool, etc. There is not a uniform format for storing parcel data across the thousands of counties and county equivalents in the U.S.; laws and regulations governing real estate/property sales vary by state. Counties and county equivalents also have inconsistent approaches to archiving historical parcel data.

To fill researchers’ needs for uniform parcel data, CoreLogic collects, cleans, and normalizes public records that they collect from U.S. County Assessor and Recorder offices. CoreLogic augments this data with information gathered from other public and non-public sources (e.g., loan issuers, real estate agents, landlords, etc.). The Stanford Libraries has purchased bulk extracts from CoreLogic’s parcel data, including mortgage, owner transfer, pre-foreclosure, and historical and contemporary tax assessment data. Data is bundled into pipe-delimited text files, which are uploaded to Data Farm (Redivis) for preview, extraction and analysis.

For more information about how the data was prepared for Redivis, please see CoreLogic 2024 GitLab.

Usage

The Property, Mortgage, Owner Transfer, Historical Property and Pre-Foreclosure data can be linked on the CLIP, a unique identification number assigned to each property.

Mortgage records can be linked to a transaction using the MORTGAGE_COMPOSITE_TRANSACTION_ID.

For more information about included variables, please see:

• core_logic_sdp_owner_transfer_data_dictionary_2024.txt
• core_logic_sdp_mortgage_data_dictionary_2024.txt
• Mortgage_v3.xlsx
• Owner Transfer_v3.xlsx

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For a count of records per FIPS code, please see core_logic_sdp_owner_transfer_counts_2024.txt and core_logic_sdp_mortgage_counts_2024.txt.

For more information about how the CoreLogic Smart Data Platform: Owner Transfer and Mortgage data compares to legacy data, please see core_logic_legacy_content_mapping.pdf.

Bulk Data Access

Data access is required to view this section.

Federal Agricultural Mortgage reported 381.41M in Interest Income for its fiscal quarter ending in March of 2025. Data for Federal Agricultural Mortgage | AGM - Interest Income including historical, tables and charts were last updated by Trading Economics this last July in 2025.

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.

AGM^C Federal Agricultural Mortgage Corporation Preferred Series C Fixed to Fltg

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

Federal Agricultural Mortgage reported 290.47M in Interest Expense on Debt for its fiscal quarter ending in March of 2025. Data for Federal Agricultural Mortgage | AGM - Interest Expense On Debt including historical, tables and charts were last updated by Trading Economics this last July in 2025.