Renters in the UK spent on average 32.5 percent of their income on rent as of January 2025. Scotland and Yorkshire and Humber were the most affordable regions, with households spending less than 28 percent of their gross income on rent. Conversely, London, South West, and South East had a higher ratio. Greater London is the most expensive region for renters Greater London has a considerably higher rent than the rest of the UK regions. In 2024, the average rental cost in Greater London was more than twice higher than in the North West or West Midlands. Compared with Greater London, rent in the South East region was about 600 British pounds cheaper. London property prices continue to increase In recent years, house prices in the UK have been steadily increasing, and the period after the COVID-19 pandemic has been no exception. Prime residential property prices in Central London are forecast to continue rising until 2027. A similar trend in prime property prices is also expected in Outer London.

The Property Valuation Data Listing offered by BatchData delivers an extensive and detailed dataset designed to provide unparalleled insight into real estate market trends, property values, and investment opportunities. This dataset includes over 9 critical data points that offer a comprehensive view of property valuations across various geographic regions and market conditions. Below is an in-depth description of the data points and their implications for users in the real estate industry.

The Property Valuation Data Listing by BatchData is categorized into four primary sections, each offering detailed insights into different aspects of property valuation. Here’s an in-depth look at each category:

1. Current Valuation AVM Value as of Specific Date: The Automated Valuation Model (AVM) estimate of the property’s current market value, calculated as of a specified date. This value reflects the most recent assessment based on available data. Use Case: Provides an up-to-date valuation, essential for making current investment decisions, setting sale prices, or conducting market analysis. Valuation Confidence Score: A measure indicating the confidence level of the AVM value. This score reflects the reliability of the valuation based on data quality, volume, and model accuracy. Use Case: Helps users gauge the reliability of the valuation estimate. Higher confidence scores suggest more reliable values, while lower scores may indicate uncertainty or data limitations.

2. Valuation Range Price Range Minimum: The lowest estimated market value for the property within the given range. This figure represents the lower bound of the valuation spectrum. Use Case: Useful for understanding the potential minimum value of the property, helping in scenarios like setting a reserve price in auctions or evaluating downside risk. Price Range Maximum: The highest estimated market value for the property within the given range. This figure represents the upper bound of the valuation spectrum. Use Case: Provides insight into the potential maximum value, aiding in price setting, investment analysis, and comparative market assessments. AVM Value Standard Deviation: A statistical measure of the variability or dispersion of the AVM value estimates. It indicates how much the estimated values deviate from the average AVM value. Use Case: Assists in understanding the variability of the valuation and assessing the stability of the estimated value. A higher standard deviation suggests more variability and potential uncertainty.

3. LTV (Loan to Value Ratio) Current Loan to Value Ratio: The ratio of the outstanding loan balance to the current market value of the property, expressed as a percentage. This ratio helps assess the risk associated with the loan relative to the property’s value. Use Case: Crucial for lenders and investors to evaluate the financial risk of a property. A higher LTV ratio indicates higher risk, as the property value is lower compared to the loan amount.

4. Valuation Equity Calculated Total Equity: based upon estimate amortized balances for all open liens and AVM value Use Case: Provides insight into the net worth of the property for the owner. Useful for evaluating the financial health of the property, planning for refinancing, or understanding the owner’s potential gain or loss in case of sale.

This structured breakdown of data points offers a comprehensive view of property valuations, allowing users to make well-informed decisions based on current market conditions, valuation accuracy, financial risk, and equity potential.

This information can be particularly useful for: - Automated Valuation Models (AVMs) - Fuel Risk Management Solutions - Property Valuation Tools - ARV, rental data, building condition and more - Listing/offer Price Determination

Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

This release presents the main results from an analysis of the profitability and resilience of farms in England using data from the Farm Business Survey. Six measures have been examined; liabilities, net worth, gearing ratios, liquidity, net interest payments as a proportion of Farm Business Income and Return on Capital Employed (ROCE).

Link to main notice: https://www.gov.uk/government/collections/farm-business-survey#documents Survey details

The Farm Business Survey (FBS) is an annual survey providing information on the financial position and physical and economic performance of farm businesses in England. The sample of around 1,900 farm businesses covers all regions of England and all types of farming with the data being collected by face to face interview with the farmer. Results are weighted to represent the whole population of farm businesses that have at least 25 thousand Euros of standard output as recorded in the annual June Survey of Agriculture and Horticulture. In 2012 there were just over 56 thousand farm businesses meeting this criteria.

The data used for this analysis is from only those farms present in the Farm Business Survey (FBS) for 2010/11 to 2012/13. Those entering or leaving the survey in this period have been excluded. The sub sample consists of around 1490 farms.

For further information about the Farm Business Survey please see: https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/series/farm-business-survey

Data analysis The results from the FBS relate to farms which have a standard output of at least 25,000 Euros. Initial weights are applied to the FBS records based on the inverse sampling fraction for each design stratum (farm type by farm size). These weights are then adjusted (calibration weighting) so that they can produce unbiased estimators of a number of different target variables.

All data in this release is based on farms present in the FBS for 2010/11 to 2012/13 and that have complete returns on their assets and liabilities. Those entering or leaving the survey in this period have been excluded. This sub sample consists of around 1490 farms. The results for this subsample have been reweighted using a method that preserves marginal totals for populations according to farm type and farm size groups. As such, farm population totals for other classifications (e.g. regions) will not be in-line with results using the main FBS weights, nor will any results produced for variables derived from the rest of the FBS (e.g. farm business income).

Measures represent a three year average from 2010-2013, presented in 2012/2013 prices (uprated according to RPI inflation). This helps to stabilise the fluctuations in income that can significantly change the financial position of a farm from year to year. Accuracy and reliability of the results

We show 95% confidence intervals against the results. These show the range of values that may apply to the figures. They mean that we are 95% confident that this range contains the true value. They are calculated as the standard errors (se) multiplied by 1.96 to give the 95% confidence interval (95% CI). The standard errors only give an indication of the sampling error. They do not reflect any other sources of survey errors, such as non-response bias.

For the Farm Business Survey, the confidence limits shown are appropriate for comparing groups within the same year only; they should not be used for comparing with previous years since they do not allow for the fact that many of the same farms will have contributed to the Farm Business Survey in both years.

We have also shown error bars on the figures in this notice. These error bars represent the 95% confidence intervals (as defined above).

For the FBS, where figures are based on less than 5 observations these have been suppressed to prevent disclosure and where they are based on less than 15 observations these have been highlighted in the tables.

Availability of results

Defra statistical notices can be viewed on the Food and Farming Statistics pages on the Defra website at https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/about/statistics. This site also shows details of future publications, with pre-announced dates.

Definitions

Mean The mean (average) is found by adding up the weighted variable of interest (e.g. liabilities or net worth) for each individual farm in the population for analysis and dividing the result by the corresponding weighted number of farms. In this report average is usually taken to refer to the mean.

Percentiles These are the values which divide the population for analysis, when ranked by an output variable (e.g. ROCE or net worth), into 100 equal-sized groups. For example, twenty five per cent of the population would have incomes below the 25th percentile.

Median The median divides the population, when ranked by an output variable, into two equal sized groups. The median of the whole population is the same as the 50th percentile.

Farm Type Where reference is made to the type of farm in this document, this refers to the ‘robust type’, which is a standardised farm classification system.

Farm Sizes Farm sizes are based on the estimated labour requirements for the business, rather than its land area. The farm size bands used within the detailed results tables which accompany this publication are shown in the table below. Standard Labour Requirement (SLR) is defined as the theoretical number of workers required each year to run a business, based on its cropping and livestock activities.

Farm size Definition Spare & Part time Less than 1 SLR Small 1 to less than 2 SLR Medium 2 to less than 3 SLR Large 3 to less than 5 SLR Very Large 5 or more SLR

Assets Assets include milk and livestock quotas, as well as land, buildings (including the farm house), breeding livestock, and machinery and equipment. For tenanted farmers, assets can include farm buildings, cottages, quotas, etc., where these are owned by the occupier. Personal possessions (e.g. jewellery, furniture, and possibly private cash) are not included.

Net worth Net worth represents the residual claim or interest of the owner in the business. It is the balance sheet value of assets available to the owner of the business after all other claims against these assets have been met. Net worth takes total liabilities from total assets, including tenant type capital and land. This describes the wealth of a farm if all of their liabilities were called in. Liabilities Liabilities are the total debt (short and long term) of the farm business including monies owed. It includes mortgages, long term loans and monies owed for hire purchase, leasing and overdrafts.

Tenant type capital Tenant type capital comprises assets normally provided by tenants and includes livestock, machinery, crops and produce in store, stocks of bought and home-grown feeding stuffs and fodder, seeds, fertilisers, pesticides, medicines, fuel and other purchased materials, work in progress (tillages or cultivations), cash and other assets needed to run the business. Orchards, other permanent crops, such as soft fruit and hop gardens and glasshouses, are also generally considered to be tenant-type capital.

Return on capital employed (ROCE) Return on capital employed (ROCE) is a measure of the return that a business makes from the available capital. ROCE provides a more holistic view than profit margins, focusing on efficient use of capital and low costs and allowing an equal comparison across farms of differing sizes. It is calculated as economic profit divided by capital employed.

Liquidity ratio The liquidity ratio shows the ability of a farm to finance its immediate financial demands from its current assets, such as cash, savings or stock. It is calculated as current assets divided by the current liabilities of the farms.

Gearing ratio The gearing ratio gives a farm’s liabilities as a proportion of its assets

Farm business income (FBI) Farm Business Income (FBI) for sole traders and partnerships represents the financial return to all unpaid labour (farmers and spouses, non-principal partners and directors and their spouses and family workers) and on all their capital invested in the farm business, including land and buildings. For corporate businesses it represents the financial return on the shareholders capital invested in the farm business. Note that prior to 2008/09 directors remuneration was not deducted in the calculation of farm business income. It is used when assessing the impact of new policies or regulations on the individual farm business. Although Farm Business Income is equivalent to financial Net Profit, in practice they are likely to differ because Net Profit is derived from financial accounting principles whereas Farm Business Income is derived from management accounting principles. For example in financial accounting output stocks are usually valued at cost of production, whereas in management accounting they are usually valued at market price. In financial accounting depreciation is usually calculated at historic cost whereas in management accounting it is often calculated at replacement cost.

Net Farm Income (NFI) Net Farm Income (NFI) is intended as a consistent measure of the profitability of tenant-type farming which allows farms of different business organisation, tenure and indebtedness to be compared. It represents the return to the farmer and spouse alone for their manual and managerial labour and on the tenant-type capital invested in the farm business.

To represent the return to farmer and spouse alone, a notional deduction is made for any unpaid labour provided by non-principal partners and directors, their spouses and by others; this unpaid labour is valued at average local market rates

Over the five years through 2024-25, the Home Insurance industry's revenue is anticipated to remain flat over the five years through 2024-25, standing at £6 billion. Revenue is comprised of premium income and investment income. Insurers must maintain enough capital reserves to meet liabilities in the event of a claim; insurers invest premium income in a diverse range of asset classes to gain a return. Despite strong demand for home insurance, supported by a rising number of housing transactions and a strong rental market, the home insurance industry has endured challenging operating conditions in recent years, including intense competition from other financial institutions like retail banks, downward pricing pressures and a tightening regulatory environment. According to ABI, premiums plummeted by 6% in 2022 amid fierce price competition and the FCA's new pricing reforms. This was despite a rise in claims following numerous severe weather events and inflated building material costs. Premiums eventually jumped in 2023-24 as insurers sought to mitigate intense cost pressures amid the inflationary environment, supporting revenue. However, a downturn in the housing market due to rising mortgage rates eroded demand for insurance, partially offsetting higher premiums. In 2024-25, premiums will continue to rise as insurers desperately try to boost profit. The UK housing market is also positioned for a strong recovery, supported by falling mortgage rates and improving economic sentiment, contributing to revenue growth of 6.6% in 2024-25. Home insurance revenue is forecast to climb at a compound annual rate of 2.7% over the five years through 2029-30 to reach £6 billion. Although premium growth is set to slow as inflation normalises, the housing market is set for solid growth in the coming years thanks to the lower interest rate environment, lifting demand for home insurance. All insurers are set to be compliant with the Solvency II reforms by December 2024, which will unlock £100 billion of investment, lifting revenue growth over the coming years. Insurtechs will continue to lead the way in innovation offering greater personalisation for customers thanks to AI and big data.