Realstats' market research focuses on transparency in the housing market. Policy and strategy influence many social issues.
By understanding housing market data, such as information on transactions, leases and rents. This allows market players and municipalities to make better and more appropriate decisions. Current policies can also be tested against the research.
For example, an analysis on the Dutch rental market. Keeping abreast of rent trendsa based on the most recent rental transactions
Full insight into price trends in a given period and/or area. Using a combination of research methods, such as desk research, observations and resident surveys
Better understanding of resident profiles in a given area. Questions such as: What should we build for whom, and who lives where? What are the moving movements in the region, where do people come from and where do people go.
"For us, the market research provided valuable insights, including to further define 'scarcity' in the housing market. The data analysis from Realstats will be included in the housing market analysis and this analysis will in turn form the basis for the new Hague housing vision with its implementation agenda."
Realstats regularly publishes market surveys on its own initiative, in cooperation with universities or knowledge institutions, or commissioned by municipalities or governments. An example of a well-known publication by Realstats is the rental monitor in cooperation with Pararius. This publication comes out quarterly.
See some of our publications below:
Rental price decline trends in largest Dutch cities (Q4-2022)
The average square metre price of a rental property in the free sector fell 1.1 per cent in the fourth quarter of 2022 compared to a year ago. This reports housing platform Pararius. Bare and furnished rental properties increased in price, while the price of unfurnished properties went down. Compared to a year ago, the number of free sector rental properties released to new tenants fell by 8.9 per cent. More information.
Academic paper: Hierarchical Bias-Aware Clustering on a Rental Price Prediction Model
In this study, a comprehensive error analysis of the Estimated Rental Value (ERV) model was done. Using Hierarchical Bias-Aware Clustering (HBAC), a new method to detect bias in predictive algorithms, data clusters on which a model underperforms in its predictions can be discovered. Dissecting these data clusters provides valuable insights into the characteristics of properties with difficult to predict rents. More information contact our data team.
Academic paper: Using a Genetic Algorithm for Hyperparameter Tuning of a Conditional Tabular Generative Adversarial Network
More data is always desirable for training machine learning models. The more data, the smarter a model becomes. This study looked at data imitation. Obtaining more data by having real data imitated by generative models, e.g. Generative Adversarial Networks (GANs). In this way, specific 'fake data' can be generated to train a model on, e.g. of objects with characteristics that are infrequent. More information contact our data team.
Slight rent increases in the free sector (Q3-2022)
The average rental price of a rental property in the free sector increased by 0.6 per cent in the third quarter of 2022 compared to a year ago. This reports housing platform Pararius. Bare and furnished rental properties increased in price, while the price of unfurnished properties remained the same. A rental property was online for 34 days on average, eight days shorter than a year ago. More information.
Free sector rent growth flattens slightly (Q2-2022)
In the second quarter of 2022, the average rental price of free sector rental properties increased by 1.3 per cent compared to a year ago. This is according to figures from housing platform Pararius. The rental market thus shows a very different picture from the owner-occupied market, where prices rose by 10.6 per cent in the second quarter of 2022. More information.
More rental monitor publications
Want to know what we can do for you? Get in touch with Philip. He will be happy to tell you about the possibilities.
Philip van der Molen
Call directly at: 010-2618300
Or mail to: p.vandermolen@realstats.com
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