Decline in housing market turnover
The number of housing market transactions declined for the first time in six years during 2019. Currently available data show an estimated 11% decrease from a year earlier. Nationwide, the number of homes sold began to decline in the second quarter (-12%) and also fell in the third quarter (-14%). The fourth quarter of 2019 saw one-fifth fewer sales than a year earlier, at the same level of processing.
In the first quarter, the number of housing transactions stagnated at the national level, with declines in larger cities.
- Budapest (-8.9%) and county seats (-4.8%) reported fewer transactions in the first three months of the year than a year earlier. The rest of the year saw a gradual decline in sales - especially in big cities. County seats showed a roughly 20% drop in transactions in the fourth quarter. Budapest experienced an even sharper decline partly due to an increase in time required for administrative procedures relating to housing.
- Smaller cities saw declines of between 8% and 10% after slight sales growth in the first quarter. Rural housing sales experienced a slight annual increase, which was also typical for the second half of 2019.
Diverging territorial processes reflect the impact of several housing market-related measures. Urban housing sales declined, at least partly, due to the July 2019 introduction of the Hungarian Government Security Plus (MÁP Plusz)1 while rural housing sales increased owing to an extension in family housing allowance (CSOK)2 grants. A village CSOK was introduced on 1 July 2019, and CSOK grants and loans also became available for buying second-hand homes.
Number of home sales and homes built for sale
Year | Home sales total | Of which: | New homes built for sale | |
---|---|---|---|---|
second-hand homes | new homes | |||
2007 | 191.2 | .. | .. | 17.9 |
2008 | 154.1 | 140.0 | 14.1 | 17.4 |
2009 | 91.1 | 82.9 | 8.3 | 16.9 |
2010 | 90.3 | 85.5 | 4.8 | 10.7 |
2011 | 8.7 | 83.9 | 3.9 | 4.8 |
2012 | 86.0 | 83.3 | 2.6 | 3.5 |
2013 | 88.7 | 8.4 | 2.3 | 3.2 |
2014 | 113.8 | 110.5 | 3.3 | 3.4 |
2015 | 134.1 | 130.7 | 3.4 | 3.1 |
2016 | 146.3 | 14.4 | 4.9 | 5.2 |
2017 | 153.8 | 147.7 | 6.1 | 7.3 |
2018 | 163.7 | 154.6 | 9.1 | 9.5 |
2019 (preliminary) | 129.4 | 123.0 | 6.4 | 12.1 |
Annual home prices keep rising
In 2019, the pure price index for second-hand dwellings stood at 169% (2015 = 100%, preliminary data), up by 17% from 2018, mainly observed in quarter 1 2019.
In 2019, the price index for new dwellings stood at 162% (2015 = 100%), up by 9.1% from the prior year, lagging behind previous years.
Trends and factors of annual price change
Year | New homes | Second-hand homes | ||||
---|---|---|---|---|---|---|
composition effect | pure change in prices | total change in prices | composition effect | pure change in prices | total change in prices | |
Previous year = 100.0 | ||||||
2016 | 97.4 | 110.5 | 107.6 | 92.9 | 113.3 | 105.3 |
2017 | 98.0 | 118.6 | 116.3 | 97.2 | 111.9 | 108.7 |
2018 | 106.5 | 113.4 | 120.8 | 97.6 | 114.2 | 111.5 |
2019 (preliminary) | 96.6 | 109.1 | 105.3 | 90.6 | 116.5 | 105.5 |
2015=100.0 | ||||||
2015 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 |
2016 | 97.4 | 110.5 | 107.6 | 92.9 | 113.3 | 105.3 |
2017 | 95.5 | 131.1 | 125.1 | 90.3 | 126.9 | 114.5 |
2018 | 101.7 | 148.7 | 151.1 | 88.1 | 144.9 | 127.7 |
2019 (preliminary) | 98.2 | 162.1 | 159.2 | 79.8 | 168.8 | 134.7 |
Intra-year reversal in price trend
At the end of 2019, second-hand home prices fell for the first time since 2014. Second-hand home prices surged period-on-period (8.6%) in quarter 1, then grew more slowly (3.3%) in quarter 2. Prices were stagnant (-0.2%) in quarter 3 and fell by a preliminary 3.0% in quarter 4. The index peaked at 171% in quarter 2 then fell to 166% by year end (2015 = 100%).
New home prices rose quarter-on-quarter by 5.2% in the first quarter and by 2.9% in the second. Preliminary data show smaller (-2.4% and -1.7%) price declines for the rest of the year. The index of new dwellings (2015 = 100%) also peaked in quarter 2 (at 165%) and fell to 158% by year end.
In quarter 4 2019, year-on-year home prices were up 8.7% in the second-hand and 3.9% in the new build sector.
Most new homes were completed in big cities
The prices of new homes completed in 2019 were largely set in contracts concluded around 2017-2018 significantly influencing new home prices. As a result, our new home price data is below the actual selling prices and only reflects the price development of actually completed dwellings.
In 2019:
- New home prices averaged HUF 29.5 million nationwide and HUF 37.3 million in Budapest, up HUF 1.9 million and HUF 3.2 million year-on-year respectively.
- Specific prices averaged HUF 480 thousand, up 11% year-on-year. Prices per square meter averaged HUF 685 thousand in Budapest, HUF 400 thousand in rural towns and HUF 371 thousand in villages.
- The new build market was spatially concentrated with barely a dozen provincial towns and five Budapest districts accounting for half of all new build sales and Budapest district 11 seeing highest new build prices at nearly HUF 800, more than double than new build prices in major provincial towns (Eger, Miskolc, Pécs, Nyíregyháza).
Specific housing prices and housing numbers in settlements and districts with most housing completions, 2019 (preliminary)
Ranking of settlements and districts | Average price, thousand HUF/m2 | Number, units |
---|---|---|
Budapest, District XIII | 652 | 507 |
Budapest, District IX | 682 | 314 |
Győr | 445 | 282 |
Debrecen | 412 | 235 |
Siófok | 597 | 224 |
Kecskemét | 429 | 223 |
Nyíregyháza | 349 | 223 |
Szombathely | 399 | 215 |
Budapest, District XI | 793 | 213 |
Pécs | 370 | 152 |
Veszprém | 448 | 136 |
Budapest, District X | 519 | 123 |
Budapest, District VIII | 781 | 115 |
Miskolc | 342 | 114 |
Szeged | 417 | 112 |
Eger | 348 | 110 |
Primarily, the Budapest agglomeration and the Lake Balaton area have seen significant housing construction in smaller settlements.
- In the Budapest agglomeration, prices per square meter of new homes averaged HUF 424 thousand, less than two-thirds of the Budapest average. New build prices were highest (HUF 480 thousand per sq m) in the north-western part of the Budapest agglomeration and lowest (HUF 398 thousand per sq m) in the southern part.
- New build prices averaged HUF 578 thousand per sq m in the southern part of the Balaton agglomeration area, almost one and a half times higher than in county seats and only 16% less than the Budapest average.
Second-hand housing market keeps changing
Our data shows that Budapest has never played such a small role (17% share) in the second-hand housing market as in 2019 with county seats stagnating and smaller settlements further increasing in terms of market share. While previously the majority of second-hand home sales took place in larger cities, in 2016 this ratio reversed. Since then, non-county seat towns and villages account for an increasing share of housing sales, so by 2019, more than 60% of all second-hand housing sales took place in these smaller settlements.
As the composition of the market shifted towards smaller settlements, regional price differences widened further.
In 2019:
- Second-hand home prices nationwide averaged HUF 17.4 million, HUF 1.4 million more than in 2018. Year-on-year, prices rose in all regions of the country. Growth was outstanding (21%) in Central Transdanubia, Budapest and the Pest region (18% and 16%, respectively), as well as in Northern Hungary (17%), which started from an extremely low base .
- The difference between the average price level of Budapest and the countryside continued to grow, exceeding 2.5 times (HUF 34.7 million and HUF 13.6 million, respectively).
- Second-hand home prices averaged HUF 5.2 million more in Budapest and HUF 3.2 million more in county seats than in 2018. Prices fell in smaller settlements in the third and fourth quarters, but rose year-on-year: a dwelling cost HUF 1.6 million more in non-county seat cities and HUF 0.7 million more in villages than a year earlier.
- Specific prices averaged HUF 263 thousand for second hand dwellings, HUF 23 thousand more than a year earlier. Prices per square meter averaged HUF 617 thousand in Budapest, exceeding the previous level by more than HUF 100 thousand.
- As the share of smaller settlements increased, the share of single-family homes increased: 54% of second-hand dwellings sold were in single-family homes. Less than a third of sales were in condominiums and a further 14% in prefab buildings.
- Prefab dwelling prices rose year-on-year fastest, averaging 20% and rising even faster in Budapest and county seats (24% and 25%, respectively). Non-prefab condominium homes became more expensive by 16% and family houses by 4.1% on average.
General price increase in the EU housing market
The Eurostat housing price index gives an aggregate view of the price development of used and new homes. In the fourth quarter of 2019, the EU-wide aggregate house price index was 122% (2015 = 100%), while the euro area house price index stood at 120%.
In the third quarter of 2019, the aggregate house price index was 165% in Hungary (2015 = 100%) according to Eurostat methodology. The Hungarian aggregate house price index rose at an outstanding rate in the first quarter, at a slower rate in the second quarter and, according to preliminary data, was slightly down in the third quarter. The consolidated housing market price index declined 2.9% in the fourth quarter, which is not unique.
Quarterly prices
- decreased by more than 3% in Denmark and Cyprus.
- fell by less than 1% in a further 7 countries.
- increased by almost or more than 3% in Malta, Estonia, Poland and Croatia.
Among neighbouring countries, in addition to the already mentioned Croatia, housing prices rose by 2.3% in Slovakia and 1.6% in both Austria and Romania. Slovenian home prices did not change significantly in the last period of the year (-0.1%).
Quarterly nominal housing price index for individual European countries (2015=100.0)
Country, group of countries | 2018 | 2019 | ||||||
---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 1 | 2 | 3 | 4 | |
Austria | 117.5 | 118.5 | 120.4 | 122.2 | 122.4 | 126.4 | 127.8 | 129.8 |
Belgium | 107.7 | 108.7 | 110.9 | 110.3 | 111.5 | 112.0 | 116.0 | 115.6 |
Bulgaria | 120.7 | 124.0 | 125.0 | 126.2 | 129.5 | 130.3 | 132.0 | 134.0 |
Croatia | 109.4 | 109.3 | 112.3 | 113.5 | 117.6 | 120.6 | 121.4 | 124.9 |
Cyprus | 103.3 | 104.0 | 103.3 | 107.0 | 107.8 | 112.3 | 105.7 | 101.8 |
Czech Republic | 125.0 | 128.7 | 131.9 | 134.4 | 137.2 | 140.6 | 143.4 | 146.4 |
Denmark | 114.0 | 115.6 | 116.1 | 113.5 | 116.0 | 118.7 | 118.9 | 115.0 |
Estonia | 115.2 | 116.4 | 116.7 | 120.0 | 122.0 | 123.1 | 126.2 | 129.8 |
Finland | 101.7 | 103.5 | 103.0 | 103.3 | 102.7 | 104.4 | 104.8 | 104.1 |
France | 105.4 | 106.3 | 108.7 | 108.5 | 108.5 | 109.7 | 112.3 | 112.6 |
Germany | 118.3 | 120.6 | 123.1 | 124.6 | 124.4 | 127.1 | 129.0 | 131.7 |
Hungary | 138.7 | 142.5 | 148.5 | 152.3 | 165.2 | 169.0 | 168.1 | 165.6 |
Iceland | 138.8 | 140.5 | 143.5 | 145.2 | 146.2 | 147.2 | 148.2 | 151.7 |
Ireland | 127.3 | 130.4 | 133.3 | 134.3 | 133.0 | 133.7 | 135.6 | 135.4 |
Italy | 98.6 | 99.2 | 98.4 | 98.3 | 97.7 | 99.1 | 98.8 | 98.6 |
Latvia | 126.0 | 129.9 | 128.3 | 132.9 | 134.0 | 140.2 | 144.6 | 145.4 |
Lithuania | 119.8 | 123.0 | 124.4 | 125.6 | 129.2 | 131.1 | 132.4 | 133.8 |
Luxembourg | 116.8 | 117.9 | 120.8 | 123.9 | 125.0 | 131.4 | 134.4 | 137.6 |
Malta | 111.3 | 115.5 | 119.3 | 123.8 | 118.5 | 122.7 | 126.4 | 130.8 |
Netherlands | 119.9 | 121.7 | 125.6 | 127.3 | 129.7 | 131.8 | 133.4 | 135.4 |
Norway | 113.6 | 116.1 | 116.3 | 116.2 | 118.5 | 121.7 | 120.5 | 120.0 |
Poland | 109.5 | 112.0 | 113.4 | 116.1 | 118.3 | 121.1 | 123.6 | 127.1 |
Portugal | 125.6 | 128.5 | 129.7 | 132.3 | 137.1 | 141.5 | 143.1 | 144.1 |
Romania | 116.1 | 119.7 | 118.9 | 119.8 | 119.9 | 121.8 | 123.6 | 125.5 |
Slovakia | 119.6 | 121.0 | 120.7 | 124.1 | 126.4 | 131.1 | 134.5 | 137.6 |
Slovenia | 117.9 | 122.4 | 122.9 | 126.7 | 127.8 | 129.4 | 133.4 | 133.2 |
Spain | 115.0 | 117.9 | 120.5 | 121.0 | 122.9 | 124.3 | 126.3 | 125.4 |
Sweden | 113.2 | 114.0 | 115.2 | 115.0 | 115.0 | 116.5 | 118.6 | 118.7 |
United Kingdom | 113.6 | 114.9 | 117.1 | 116.6 | 115.3 | 116.1 | 118.1 | 117.8 |
EU-28 average | 112.4 | 114.1 | 115.9 | 116.6 | 117.1 | 119.0 | 120.7 | 121.5 |
Euro area | 111.3 | 113.0 | 114.8 | 115.5 | 115.9 | 117.9 | 119.5 | 120.4 |
Methodological notes
The cumulative values of the published housing market indices are also included in the housing price indices of Eurostat3. Due to the harmonised methodology, these data are fully comparable across the European countries as well as with the aggregated indices of the EU member states.
The source of price observations is the stamp duty database of the National Tax and Customs Administration of Hungary (NAV), from where the anonymized stamp duty data are taken over on a monthly basis directly after their receipt. All home sales concluded by private individuals are subject to this data transfer including home sale prices and the most important characteristics. Through the development, information on housing market transactions was supplemented with information available in the statistical registers of HCSO and relevant for the housing market processes. This gives us more accurate information on the type of dwellings sold on the market and their immediate environment. At present, there are data series of uniform structure comparable in every respect from 2007, which make it possible to analyse changes in home prices in a more detailed and exact way. From 2016 onwards, data received include the nationality and birth year of the given home buyer. The gradually completed data base still allows only preliminary information on the processes of 2018. Our compilation’s data for the period prior to 2018 are final.
As a result of missing data, 1.0 per cent of all cases were excluded from calculations. In those cases, where there were no data on the floor area of the given dwelling, but all other data were available, the floor area was estimated using the home price and its other characteristics, then we used this estimated value to further calculate. Following this, a log linear regression model was used to analyse the data. Major data used in this model: floor area of the given dwelling, character of the building, specific geographical, administrative and income indicators of the given settlement (or district in Budapest) and the characteristics of the immediate neighbourhood zone and the residential building. New dwellings were separated by NAV based on benefits used to buy a new dwelling. From 2018, data collection OSAP (National Statistical Data Collection Programme) 1078 is also used to identify new dwellings by using its data on buildings constructed for sale and received a put to use permit.
The number of transactions available for monitoring the new housing market is still low in the actual period, so the preliminary nature of the results for new housing should be emphasized.
Based on the findings of the first model estimation a further 5.0 per cent of dwellings were filtered out as outliers from further index calculations. After the exclusion of outliers, based on repeated model estimations, changes in prices were broken down by the composition effect and pure changes in prices. As a result of the log linear method the released price indices resulted from the geometrical average of the given prices in all cases. However, the average prices of this publication are always arithmetical averages, which were calculated after the completion of the outlier filtering.
The Eurostat’s aggregated housing price index is the weighted average of the price indices of second hand homes and new homes presented in our publication. The weights are the aggregate values of home sales realized in the previous year. The most recent Hungarian data published by Eurostat are always preliminary results based on the data recorded by the end of the second month following the reference period, while to this present publication we have used data received for the complete quarter following the reference period.
Further data, information
2.3.6. Housing price indices
2.3.7. Number of housing transactions made by private persons by quarter years
6.2.2.8. Mean price per dwelling by region and settlement type
6.2.2.9. Mean price per sqm by region and settlement type
6.2.2.10. Number of housing transactions made by private persons by region and settlement type
6.2.2.11. Mean price per dwelling by region and building type
6.2.2.12. Mean price per sqm by region and building type
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