© Hollandse Hoogte / Lex van Lieshout

Measuring commercial property prices

Is the ubiquitous vacancy of retail space reflected in its property price developments? And how have the prices of office buildings developed since the credit crisis? In order to find answers to these types of questions about commercial real estate, Statistics Netherlands (CBS) conducts research in collaboration with the Land Registry Office (Kadaster) and the Dutch central bank (DNB). The commercial real estate market is an important indicator for financial stability, which means that insight into its price developments is very much needed.

Within the framework of broader research into commercial property price indicators (CPPIs), this project focuses on calculating price developments of leased residential, industrial, office and retail buildings. This research is innovative because of its increased coverage, as it includes portfolio transactions. The interim results of this research are presented on this innovation page. Research data, method and models will be further optimised in the future. There is little (inter)nationally comparable research available. Feedback on this research is therefore very much welcomed. A more in-depth description of the research, explained in the following summary, can be found in the section on methodology (Dutch only).

Definition and data

In this study, commercial real estate is defined as all leased residential and non-residential properties. The focus here is on any rental housing and industrial, office and shop premises that were part of a real estate transaction. A price index has been calculated for each of these four property types.

It is a complex task to establish the transaction value of each commercial property which was sold within a given period. Commercial property transactions often include multiple properties at once. This means an entire real estate portfolio is sold in a single transaction. The price stated in the notarial deed for such transactions may concern all the properties or only a selection of the sold properties. Moreover, movable properties - e.g. cars, machinery, other inventory - may have been included in the transaction price. In short, the Cadastral registration does not necessarily list one single, unequivocal transaction price for each traded property.

For identification of single properties and their associated transaction price, CBS has developed around 25 decision rules. An example of such a decision rule is a check on the notarial deed to see whether it mentions single or multiple properties. If it mentions a single property then this serves as a first indication that it concerned a one-to-one transaction. If multiple properties were listed in one deed, this is a first indication that it concerned a so-called portfolio transaction.

This research is innovative in the sense that portfolio transactions were incorporated into the index calculation. Portfolio (bulk) sales are property transactions in which multiple objects are traded simultaneously at a single transaction price. This type of transaction takes up a sizeable chunk of the trade in commercial real estate, as shown in the figure below. By incorporating portfolio transactions, the price index represents the actual real estate market more adequately.

Number of real estate objects sold (%)
 Individual transactionPortfolio transaction
Office29.270.8
Industrial58.741.3
Retail30.669.4
Rental housing24.375.7
Source: CBS, Kadaster

The portfolio transaction price refers to all the objects within one transaction. This means no separate price is known per object, but that is what this calculation method requires. In addition, it is desirable to apply the same unit of measurement to each transaction (either a single object or a single transaction) while the original source data must be taken into account as much as possible (e.g. what was sold for what price). Simply choosing one or the other would result in significant loss of data, which would reduce the reliability of the calculated index. Hence, the following steps have been defined for processing the transactions:

  • One-to-one transactions: properties which are not sold simultaneously with other goods form a simple category. These observations can easily be included in the calculation of the index without further ado.
  • Simple portfolio transactions: portfolios in which a set of indicators is (nearly) identical for all the traded objects (i.e. the type of real estate, location, date of construction) are included in the index calculation as being one transaction only.
  • Complex portfolio transactions: portfolios in which the set of indicators varies between the traded objects, cannot be treated as a single transaction. For example, those portfolios which include both office and residential premises and in different municipalities. In such cases, the transaction price per object is estimated before it is included in the index calculation.

The primary source for this research is the Cadastral Key Register (Basisregistratie Kadaster or BRK). It contains the transaction price, transaction date and object characteristics of all transactions. These data have been supplemented by object characteristics taken from the Addresses and Buildings key register (BAG). To enable estimation of individual object prices within a portfolio transaction, another used source is the Key Register for Real Estate Valuation (WOZ). Finally, neighbourhood characteristics and territory alignments are used which are collected every year by CBS and published on StatLine.

Method and model

Real estate objects may display major differences in terms of their characteristics such as size, construction date and location. Furthermore, the composition of a group of properties may vary from one period to the next. In developing reliable price measurements despite these variations, the price indices have been calculated using a hedonic regression model. This entails calculating the effect of each object characteristic on the transaction price. Price developments have been adjusted for such quality changes. In order to distinguish these trend-like developments from random price fluctuations, trend lines have been calculated subsequently according to a state space model. Finally, the bootstrap method was used to calculate a 95% reliability margin around the trend line. This involves determining the variance by repeating the price index calculation a large number of times for each subsequent period.

Subsequently, the best possible selection is made of price-determining characteristics for each of the four types of real estate. In order to determine the most appropriate selection, a large number of combinations of multiple variables are tested, checks are performed and statistical fallout is analysed. The table below represents the chosen selection of characteristics for each type of real estate. A description of the characteristics is provided in chapter five of the method description.

 Table 1 – Overview of the selected models per property type

picture of Overview of the selected models

Another innovative aspect of this research project is related to the coverage of the price developments. In the calculation process, all transactions were used which were registered at the Land Registry Office. This ensures that the price indices cover the entire country. However, it does not mean that all the observations are actually included in the final calculation process. After all, observations may not be taken into account for a large number of reasons. This happens for example, when one specific characteristic is missing, when it is not possible to estimate a (realistic) individual transaction price in a portfolio, or when it is not possible to determine with certainty whether the sales price is associated with the property in the transaction. Nevertheless, the highest possible number of observations is included based on the decision-making rules. Depending on type of property, nationwide coverage varies between approximately 80 and 90 percent.

The figure below presents the regional results: the share of incorporated observations at local level. The number of sold objects that were included into the calculation was expressed as a percentage of the total number of sold objects per category of commercial property. These are individual objects, with portfolio transactions broken down into individual transactions. The selections of data and research model were aimed at keeping the nationwide distribution of included observations as uniform as possible. Click on the type of property to view regional coverage rates per type.

Objects incorporated into the calculation, by property type (% of objects sold)
GemeentenaamStatcodeIndustrialRetailRental dwellings
Appingedam78.988.297.344.1
Bedum88.986.495.769.0
Bellingwedde75.0100.075.064.6
Ten Boer100.0100.083.396.4
Delfzijl78.385.792.678.9
Groningen81.479.388.789.6
Grootegast100.088.942.190.9
Haren94.788.9100.097.5
Hoogezand-Sappemeer69.265.388.370.7
Leek95.592.092.567.7
Loppersum75.073.3100.074.2
Marum100.088.284.690.2
Almere54.597.481.979.5
Stadskanaal91.784.358.889.8
Slochteren58.890.072.082.9
Veendam79.679.578.994.1
Vlagtwedde75.063.589.768.6
Zeewolde92.291.794.492.9
Winsum100.066.7100.031.1
Zuidhorn75.080.670.075.5
Dongeradeel68.283.987.576.3
Achtkarspelen85.086.164.366.1
Ameland92.9100.085.2
het Bildt100.091.569.257.3
Franekeradeel96.084.993.064.5
Harlingen73.985.583.953.2
Heerenveen87.280.686.388.0
Kollumerland en Nieuwkruisland80.085.760.082.0
Leeuwarden86.588.781.486.1
Leeuwarderadeel90.094.1100.087.1
Ooststellingwerf83.376.375.492.2
Opsterland100.083.384.237.3
Schiermonnikoog100.0100.080.083.3
Smallingerland91.693.287.778.4
Terschelling75.091.783.39.7
Vlieland80.0100.071.415.8
Weststellingwerf81.081.466.758.3
Assen88.095.190.288.2
Coevorden75.860.578.681.4
Emmen81.767.379.581.1
Hoogeveen72.278.073.870.5
Meppel88.592.280.555.9
Littenseradiel68.435.5100.094.7
Almelo80.591.791.783.5
Borne60.094.071.747.5
Dalfsen5.986.36.43.8
Deventer89.890.784.575.4
Enschede68.586.482.969.1
Haaksbergen100.093.873.895.1
Hardenberg91.278.591.775.5
Hellendoorn88.995.878.463.9
Hengelo57.885.987.176.7
Kampen93.791.894.381.3
Losser73.387.0100.078.4
Noordoostpolder91.478.787.070.9
Oldenzaal91.789.789.889.7
Ommen94.496.278.773.4
Raalte94.194.791.975.7
Staphorst93.190.394.788.1
Tubbergen81.089.788.989.3
Urk100.098.195.040.3
Wierden87.087.191.984.8
Zwolle86.686.280.472.2
Rijnwaarden100.0100.0100.092.9
Aalten86.288.290.562.5
Apeldoorn82.184.083.373.7
Arnhem88.087.976.179.7
Barneveld97.697.892.060.8
Beuningen70.089.494.581.7
Brummen88.288.4100.097.4
Buren82.498.2100.081.8
Culemborg96.198.794.773.3
Doesburg85.762.5100.051.8
Doetinchem76.392.481.084.8
Druten95.296.6100.070.8
Duiven23.492.8100.097.4
Ede86.289.191.178.9
Elburg92.398.593.361.9
Epe95.598.195.278.6
Ermelo89.789.789.672.9
Geldermalsen76.088.082.447.0
Harderwijk95.294.698.689.4
Hattem60.0100.078.628.7
Heerde95.5100.0100.040.2
Heumen94.785.395.271.8
Lochem100.092.097.289.1
Maasdriel79.292.6100.087.6
Nijkerk89.997.691.164.4
Nijmegen80.688.784.379.4
Oldebroek88.993.283.345.5
Putten100.097.389.769.5
Renkum2.4100.00.03.1
Rheden92.092.193.785.8
Rozendaal100.0
Scherpenzeel92.095.394.196.2
Tiel85.586.992.288.5
Voorst94.979.687.955.8
Wageningen97.984.085.261.4
Westervoort93.3100.097.794.7
Winterswijk100.083.885.776.3
Wijchen75.890.486.090.8
Zaltbommel50.395.796.286.4
Zevenaar86.890.284.166.8
Zutphen85.489.194.056.8
Nunspeet88.996.787.077.9
Dronten98.189.698.686.8
Neerijnen93.997.0100.084.0
Amersfoort75.090.577.674.5
Baarn88.396.886.283.6
De Bilt96.292.993.197.1
Bunnik100.0100.0100.094.3
Bunschoten80.895.395.874.1
Eemnes0.087.50.00.0
Houten61.486.799.180.7
Leusden89.995.586.089.1
Lopik81.887.892.976.7
Montfoort100.093.196.394.0
Renswoude87.595.387.565.5
Rhenen100.096.296.875.0
Soest77.088.876.090.6
Utrecht84.588.994.885.7
Veenendaal83.489.665.176.0
Woudenberg91.790.995.091.9
Wijk bij Duurstede52.086.397.175.4
IJsselstein74.192.176.771.6
Zeist89.993.883.976.2
Nieuwegein87.692.951.786.0
Aalsmeer97.186.486.475.3
Alkmaar96.897.183.688.2
Amstelveen0.089.00.00.0
Amsterdam80.881.883.389.5
Beemster83.394.1100.057.7
Bergen (NH.)93.995.798.197.2
Beverwijk92.293.978.674.6
Blaricum0.092.90.00.0
Bloemendaal100.0100.081.888.6
Castricum85.7100.0100.084.7
Diemen89.898.492.891.8
Edam-Volendam93.898.395.577.4
Enkhuizen97.298.795.880.6
Haarlem89.392.293.890.2
Haarlemmerliede en Spaarnwoude100.042.9100.039.0
Haarlemmermeer84.688.396.488.2
Heemskerk25.094.387.890.8
Heemstede100.095.082.095.9
Heerhugowaard65.098.155.171.0
Heiloo50.079.798.597.3
Den Helder77.993.088.968.1
Hilversum78.684.377.482.3
Hoorn76.198.195.580.2
Huizen60.089.471.470.8
Landsmeer100.097.4100.087.2
Langedijk97.596.997.189.8
Laren0.0100.00.00.0
Medemblik93.995.486.174.7
Oostzaan0.0100.085.787.5
Opmeer33.394.095.0100.0
Ouder-Amstel64.261.377.8100.0
Purmerend96.196.596.291.3
Schagen92.586.879.088.8
Texel92.388.9100.094.4
Uitgeest83.395.891.773.0
Uithoorn100.096.995.293.8
Velsen68.396.096.477.4
Weesp88.192.796.491.3
Zandvoort100.094.694.194.1
Zaanstad78.088.687.281.1
Alblasserdam100.099.498.092.0
Alphen aan den Rijn82.690.463.181.7
Barendrecht49.598.485.669.4
Drechterland54.589.697.149.7
Brielle95.284.392.990.0
Capelle aan den IJssel24.596.498.799.6
Delft79.760.077.292.6
Dordrecht74.292.674.387.1
Gorinchem75.096.897.658.6
Gouda64.386.878.085.6
's-Gravenhage84.689.781.388.5
Hardinxveld-Giessendam93.192.092.377.9
Hellevoetsluis94.198.596.893.2
Hendrik-Ido-Ambacht93.894.890.090.6
Stede Broec100.097.297.667.4
Hillegom77.394.494.993.4
Katwijk33.388.189.092.0
Krimpen aan den IJssel94.699.173.186.0
Leerdam100.097.493.469.6
Leiden78.795.487.468.8
Leiderdorp97.4100.084.695.8
Lisse100.095.397.984.0
Maassluis100.083.997.092.4
Nieuwkoop75.097.893.149.0
Noordwijk86.394.598.296.5
Noordwijkerhout100.095.391.392.6
Oegstgeest64.775.050.090.1
Oud-Beijerland94.896.396.781.2
Binnenmaas100.090.487.089.5
Korendijk75.097.4100.078.6
Oudewater92.397.286.762.5
Papendrecht84.298.194.899.2
Ridderkerk65.597.685.993.9
Rotterdam85.593.193.692.7
Rijswijk4.367.835.739.5
Schiedam76.485.280.395.2
Sliedrecht66.293.593.489.6
Cromstrijen100.097.896.395.3
Albrandswaard83.957.5100.085.7
Westvoorne100.093.575.899.0
Strijen100.094.388.994.0
Vianen75.983.990.389.9
Vlaardingen76.095.295.876.8
Voorschoten96.598.4100.086.5
Waddinxveen86.391.792.177.6
Wassenaar100.092.798.292.5
Woerden62.696.188.991.1
Zoetermeer86.397.585.986.3
Zoeterwoude0.097.415.06.1
Zwijndrecht77.791.995.397.2
Borsele91.794.388.284.5
Goes91.788.168.072.0
West Maas en Waal77.893.292.353.1
Hulst95.775.587.063.5
Kapelle100.090.588.976.2
Middelburg98.399.582.689.5
Giessenlanden100.098.594.798.3
Reimerswaal77.892.281.878.8
Zederik100.089.7100.095.1
Terneuzen87.084.185.588.2
Tholen92.394.284.484.1
Veere100.098.593.273.6
Vlissingen72.779.693.080.4
Lingewaal100.0100.0100.069.4
De Ronde Venen7.896.840.047.8
Tytsjerksteradiel85.476.682.477.9
Aalburg100.091.592.375.0
Asten100.095.9100.074.2
Baarle-Nassau78.682.488.287.2
Bergen op Zoom89.289.189.362.9
Best83.795.389.568.5
Boekel75.092.1100.055.6
Boxmeer96.493.595.277.9
Boxtel93.991.984.476.2
Breda85.886.980.984.9
Deurne87.187.589.181.3
Pekela90.947.680.823.1
Dongen78.280.8100.076.8
Eersel90.991.363.677.2
Eindhoven72.786.681.988.1
Etten-Leur87.381.157.488.5
Geertruidenberg100.087.597.693.5
Gilze en Rijen82.894.292.368.8
Goirle92.993.783.958.8
Grave87.560.0100.093.9
Haaren100.094.770.091.6
Helmond92.592.984.877.7
's-Hertogenbosch73.587.288.460.5
Heusden94.588.181.473.6
Hilvarenbeek100.096.592.969.0
Loon op Zand100.093.9100.053.1
Mill en Sint Hubert100.091.470.072.0
Nuenen, Gerwen en Nederwetten100.096.860.376.5
Oirschot96.698.3100.074.3
Oisterwijk83.397.693.767.7
Oosterhout83.589.796.476.9
Oss91.590.691.590.8
Rucphen90.986.7100.094.3
Sint-Michielsgestel82.493.292.180.5
Someren80.889.6100.041.7
Son en Breugel100.097.9100.091.0
Steenbergen85.789.681.689.6
Waterland90.996.291.794.8
Tilburg0.092.90.00.0
Uden95.698.095.075.2
Valkenswaard87.193.873.876.1
Veldhoven84.991.846.383.1
Vught98.298.397.995.6
Waalre93.1100.069.279.5
Waalwijk79.087.590.781.5
Werkendam82.184.381.883.4
Woensdrecht89.582.192.374.0
Woudrichem100.097.5100.086.0
Zundert100.081.893.990.6
Wormerland94.4100.0100.092.2
Onderbanken100.050.068.5
Landgraaf86.791.748.275.2
Beek91.987.597.987.6
Beesel100.075.082.490.4
Bergen (L.)100.092.395.567.1
Brunssum85.748.688.489.2
Gennep85.789.875.081.0
Heerlen86.669.185.386.8
Kerkrade76.381.458.179.6
Maastricht56.178.984.384.1
Meerssen94.494.193.892.5
Mook en Middelaar100.084.6100.078.2
Nederweert85.791.590.972.9
Nuth94.196.9100.087.3
Roermond85.584.590.986.9
Schinnen100.084.684.679.9
Simpelveld75.044.492.972.0
Stein65.089.5100.073.3
Vaals92.383.392.686.1
Venlo63.385.375.490.3
Venray0.081.10.00.3
Voerendaal100.084.086.466.2
Weert75.673.786.469.6
Valkenburg aan de Geul92.988.9100.089.6
Lelystad76.792.295.190.9
Horst aan de Maas86.778.184.268.4
Oude IJsselstreek75.689.495.577.1
Teylingen100.094.597.891.9
Utrechtse Heuvelrug88.397.194.184.5
Oost Gelre85.491.888.276.6
Koggenland92.089.3100.084.3
Lansingerland98.995.694.279.6
Leudal92.095.796.288.6
Maasgouw93.881.384.668.0
Eemsmond59.374.389.549.4
Gemert-Bakel89.592.990.979.4
Halderberge97.993.394.781.7
Heeze-Leende100.0100.093.886.0
Laarbeek95.882.5100.081.3
De Marne75.081.580.073.0
Reusel-De Mierden100.090.9100.079.1
Roerdalen78.659.321.475.2
Roosendaal89.394.187.884.0
Schouwen-Duiveland91.995.891.588.4
Aa en Hunze87.571.184.693.8
Borger-Odoorn85.764.277.888.8
Cuijk63.497.782.495.3
Landerd81.876.982.672.4
De Wolden70.692.5100.068.8
Noord-Beveland83.379.477.833.0
Wijdemeren97.598.993.262.5
Noordenveld85.786.989.563.4
Twenterand92.695.280.584.1
Westerveld88.987.581.864.2
Sint Anthonis88.966.7100.068.6
Lingewaard84.077.177.358.7
Cranendonck93.993.387.579.0
Steenwijkerland82.990.192.354.2
Moerdijk47.995.084.087.9
Echt-Susteren96.091.597.484.6
Sluis94.490.890.881.6
Drimmelen100.090.491.380.4
Bernheze0.092.90.00.0
Ferwerderadiel100.038.166.782.8
Alphen-Chaam100.082.8100.071.4
Bergeijk100.098.7100.079.0
Bladel97.194.689.285.5
Gulpen-Wittem100.070.675.082.6
Tynaarlo94.485.194.468.5
Midden-Drenthe96.090.586.474.7
Overbetuwe93.395.286.773.3
Hof van Twente96.991.985.577.9
Neder-Betuwe93.385.887.972.4
Rijssen-Holten90.396.486.666.1
Geldrop-Mierlo97.992.288.090.1
Olst-Wijhe100.084.492.373.1
Dinkelland100.085.388.595.5
Westland86.490.793.867.0
Midden-Delfland100.096.470.678.2
Berkelland77.576.074.282.9
Bronckhorst88.188.097.786.8
Sittard-Geleen86.690.888.484.7
Kaag en Braassem63.285.193.184.2
Dantumadiel100.083.396.892.9
Zuidplas50.097.840.243.2
Peel en Maas88.787.279.287.8
Oldambt64.945.670.866.0
Zwartewaterland81.892.592.983.2
Súdwest-Fryslân81.888.080.276.9
Bodegraven-Reeuwijk83.587.770.371.0
Eijsden-Margraten42.960.082.652.1
Stichtse Vecht86.095.794.489.3
Menameradiel100.094.4100.084.3
Hollands Kroon91.776.584.558.1
Leidschendam-Voorburg97.090.196.197.6
Goeree-Overflakkee93.391.982.174.6
Pijnacker-Nootdorp92.097.999.145.2
Molenwaard92.3100.085.367.7
Nissewaard46.398.184.365.2
Krimpenerwaard88.793.486.972.7
De Fryske Marren92.982.294.172.0
Gooise Meren86.284.379.478.5
Berg en Dal85.792.966.777.4
Meierijstad0.095.90.00.0
Montferland96.092.292.078.6
Menterwolde83.385.2100.079.1
Source: CBS, Kadaster

Results

All four types of property show a price decline until 2014/2015. This was followed by a price increase. Only retail space prices remained somewhat stable, rising less sharply than rented dwellings, industrial estate and office space. The figure below shows quarterly price indices by property type as well as the trend line with the corresponding 95% confidence interval. The price indices have 2015 as the reference year. Click on the buttons in the legend to view the different price indices and trend lines.

Show category: Source: CBS, Kadaster
Margins are 95% confidence intervals.

Privacy

For this research, real estate transactions were taken from the Cadastral Key Register, supplemented by data from the Addresses and Buildings key register. In this research, we used the transaction prices and characteristics of the real estate objects. The results do not divulge any traceable information about the parties in the transaction (the buyer and the seller). This study furthermore incorporates the WOZ value of real estate objects. However, this was done in an exclusively and indirect manner, for example to enable a proportional breakdown of the transaction price into the underlying objects. The WOZ value of the objects themselves cannot be traced from the results.

Feedback

CBS welcomes your opinion about this research. You can send us your suggestions for improvement of this method or to refine the models that were implemented here. Please let us know if you see something missing that you think is relevant. Use the form below to submit your feedback. Thank you!