Household Income: Rent Effort Rate Increases in All Provinces

He percentage of household income necessary for deal with renting a house it increased in all the provinces, going from 26.4% last year to 29.4%. According to a study published by idealista carried out by comparing rental prices in September 2022 and the estimate* of family income on that same date.

Family income dedicated to paying rent increases during the last year

Barcelona is the province where the effort has increased the mostor, by going from a rate of 43% of family income in the third quarter of 2021 to 52.2% this year. They are followed by the increases in Girona (from 33.3% to 40.8%), Cantabria (from 29% to 36%), Madrid (from 35% to 41.1%), Guipúzcoa (from 43.2% to 48 8%), Segovia and the Balearic Islands (both going from 26% to 31.6%).

On the opposite side, the smallest increases in the effort to rent a home occurred in Ourense (from 21.5% to 22.4%), Jaén (from 19.8% to 21.2%) and Cáceres (from 21, 3% to 22.9%).

The province of Barcelona is the one that requires the greatest effort from its citizens, since it is necessary to allocate 52.2% of family income to paying rent. It is followed by Guipúzcoa, with 48.8%, Las Palmas (42%), Vizcaya (41.2%), Madrid (41.1%), Girona (40.8%), Cantabria (36%), Valencia ( 35.3%), Seville (34.7%), Lleida (33.7%), Álava (33.5%) and Santa Cruz de Tenerife (33.4%). The 12 provinces exceed the threshold of one third of the income, which is the limit recommended by experts.

The least effort, on the other hand, is in the province of Jaén, with 21.2%. This is followed by Lugo (21.9%), Ourense (22.4%), Teruel (22.4%) and Cáceres (22.9%).

In the city of Barcelona, ​​the effort rate shoots up to 58.4%

Barcelona is, by far, the capital where the effort has grown the most necessary, since it has gone from 36.1% in September 2021 to 58.4% in September 2022. It is followed by the increase in Madrid (from 33.7% to 42.1%) and then the cities of Valencia (from 27.2% to 34.8%), Málaga (from 26.5% to 33.2%), San Sebastián (from 33.9% to 40.4%), Alicante (from 24% to 30.1%) and Palma (from 24.4% to 29.8%).

There have been three capitals in which the effort has been reduced: in Melilla (from 34.3% to 32.1%), Badajoz (from 23.9% to 23.5%) and Córdoba (from 28.2%). % to 28.1%).

A total of eight capitals require an effort greater than a third of the income to pay the rent. Barcelona, ​​with 58.4%, is the one that absorbs the most family resources, followed by Madrid (42.1%), Ceuta (41.9%), San Sebastián (40.4%), Bilbao (39%), Las Palmas de Gran Canaria (36%), Valencia (34.8%) and Vitoria (33.8%).

The lowest effort rate, on the contrary, is found in Cáceres (22.1%), Pontevedra (22.3%), Ourense (22.3%), Lugo (22.6%), Ciudad Real (23 %) and Albacete (23.4%).

* Estimation Methodology Net Household Income and Effort Rates

The effort rate measures the weight of the home on the purchasing power of the home, for this reason our calculations are made based on the value of the home, whether it is for sale or rent, along with our estimates of net family income. In particular, in the case of rent, we measure the effort rate as the annual share of the household’s net income that goes towards rent. In the same way, in the case of buying and selling, the effort rate is calculated as the annual share of the household’s net income that is used to pay a “typical” mortgage, in the sense that it is stipulated with average characteristics in terms of duration and interest rate. Due to the recent increases in interest rates, the calculation has been updated taking into account the latest data published by the ECB.

The values ​​for sale and rent come directly from the idealista data source, which has average prices for each city. On the contrary, in the case of net family income, in the absence of updated official data for each city, we use our battery of machine learning models that combine information from various socioeconomic metrics from different sources (public and idealista). Our machine learning models are essentially of the random forest type and with gradient boosting (CatBoost), and are trained with data accessible to the public: average income per household at the municipal level and census section from the Household Income Distribution Atlas (INE). , annual frequency 2015-2019, link), and average income per household at the national level and by autonomous community from the Living Conditions Survey (INE, annual frequency, 2020 last year available, link). Once the models have been trained, the inference is generated to be able to impute income levels per household on other segmentations or locations.

Our models allow us to obtain a reliable estimate of the level of income relatively quickly (quarterly frequency and without publication delay) and with a high level of territorial breakdown, obtaining estimates for each neighborhood of each city in Spain, Italy, and Portugal. Important, we regularly check and review our models so that they always maintain a high level of precision and reliability.