Real Estate Brokers: Do they inflate housing prices? - CEREG

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Indeed, investors can deduct 6% of the price during the first seven years and 4% during thefollowing two years. Moreover, the rents benefit from 6 % abatement. Ultimately, with thislaw, an investor can deduct up to 65% of the amount invested. The law did increaseinvestment in the new <strong>housing</strong> market and especially for small accommodations.Table 1 │ Variable descriptionsIntermediationAgent-assisted transaction (RE): Takes the value 1 if a real estate broker is engaged in the transaction and 0otherwise.Hedonic characteristicsSale price (PM2): Price per square meters in euros paid by a buyer for a house.City: A vector of city location variables, equalling 1 if the house is situated in that city and 0 otherwise. Thecities are: Bordeaux, Lille, Lyon (9 urban districts), Marseille (16 urban districts + one category forunknown district), Montpellier, Nancy, Nantes, Rennes, Roubaix, Strasbourg, Toulouse, and Tourcoing. Wereunited the cities of Lille, Roubaix and Tourcoing as <strong>they</strong> are very near to each otherStreet type: A vector of street type variables: alley, avenue, blind alley, boulevard, crossroad, path, park,public garden, square, street, residence, road, etc.Apartment type (St, Lo, Du, Tr): A vector of apartment type variables (standard, loft, duplex, triplex).Rooms (Rx): A vector representing the number of rooms in the house: from 1 to 8. Houses with more thaneight rooms are set in the 8 and more room category.Floor (Fx): A vector representing the floor number: from 0 to 9, floors 10 to 19 are put in the same category,and the last category contains floors 20 and above.Month of sale (Mx): A vector representing the month in which the closing of sale occurred.Number of cellars in the house (Cellar)Number of bathrooms in the house (Bath)Construction period (A to H): A vector of categorical variables representing the construction period. Thecategories are: < 1850; 1851-1913; 1914-1947; 1948-1969; 1970-1980; 1981-1991; 1992-1999; 2000-2005.Terrace (Terrace): An indicator variable that takes the value 1 if there is a terrace and 0 otherwise.Balcony (Balcony): An indicator variable that takes the value 1 if there is a balcony and 0 otherwise.Loggia (Loggia): An indicator variable that takes the value 1 if there is a loggia and 0 otherwise.Storeroom (Storeroom): An indicator variable that takes the value 1 if there is a storeroom and 0 otherwise.Attic (Attic): An indicator variable that takes the value 1 if there is a attic and 0 otherwise.Buyers’ and sellers’ characteristicsSocio-occupational category (SOCx): A vector of categorical socio-occupational variables, equalling 1 if theoccupation of the agent falls into that category, 0 otherwise. The categories are: Farmers; Artisans,shopkeepers, and employers; Managers and higher-grade occupations; Intermediate-grade occupations;Clerical workers; Manual workers; Retired persons; Other persons without occupational activity.Matrimonial status: A vector of categorical variables that represents the matrimonial status and the genderof the person who signs the contract. The categories are: married, single, divorced, remarried, PACScontract which corresponds to a legal contract concluded between two adults of opposite sex or not inorder to organize their common life (equivalent of common-law), and widowed. Each category is dividedinto man and woman.Age (AGExx): A vector of categorical variables that represents the age of the agent. The categories are: 18-29 years; 30-39 years; 40-49 years; 50-59 years; 60-69 years; 70-79 years; 80 years and up.Nationality: A vector representing different nationality: Algeria; Belgium; France; Germany; Italy;Morocco; Portugal; Spain; Tunisia; Turkey; United Kingdom; others.12
The number of rooms is included to control for the fact that the bigger the apartment, thesmaller the price per square meter. Also, small accommodations are more frequent and aremore demanded in cities than big apartments which can be found usually in the suburbs.Table 2a │ Summary statistics of hedonic characteristicsBroker-AssistedTransactionsNon-Broker-AssistedTransactionsFull SampleProportion of Broker-AssistedTransactions in Full SampleHouse CharacteristicsPM2 (Purchase price)2195.47€(606.34)*2148.56€(678.21)*2181.70€(626.37)*Lyon (%) 21.4 21.4 21.4 70.6Marseille (%) 13.3 20.5 15.4 61.0Lille/ Roubaix/Tourcoing (%) 7.9 4.9 7.0 79.5Nancy (%) 3.2 3.3 3.2 69.4Toulouse (%) 13.3 13.4 13.4 70.5Montpellier (%) 3.7 3.5 3.6 71.8Rennes (%) 9.2 7.6 8.7 74.5Nantes (%) 12.4 9.6 11.5 75.7Bordeaux (%) 7.9 6.5 7.5 74.6Strasbourg (%) 7.8 9.3 8.3 66.8Standard (%) 95.1 95.7 95.3 70.5Loft (%) 0.1 0.1 0.1 72.7Duplex (%) 4.7 3.9 4.4 74.3Triplex (%) 0.2 0.3 0.2 52.4Number of rooms 2.9 2.8 2.9 //Floor number 2.8 2.7 2.7 //Number of cellars 0.6 0.6 0.6 //Number of bath rooms 1.1 1.0 1.0 //A (%) 2.4 2.3 2.4 71.4B (%) 6.7 7.1 6.8 69.5C (%) 9.6 10.7 9.9 68.3D (%) 23.0 29.1 24.8 65.5E (%) 15.2 16.4 15.5 69.1F (%) 14.4 14.6 14.5 70.3G (%) 11.4 8.2 10.4 76.9H (%) 17.4 11.6 15.7 78.3Terrace (%) 7.5 6.8 7.3 72.6Balcony (%) 21.9 20.9 21.6 71.7Loggia (%) 13.0 14.4 13.4 68.5Storeroom (%) 15.6 16.2 15.8 69.8Attic (%) 7.0 6.2 6.7 73.1* The values in parentheses are standard error.Number of Observations: 7495 broker-assisted, 3115 non-agent-assisted, 10610 total//The decision to use a broker may also depend on the type of apartment. Duplexes andtriplexes may be harder to sell than standard houses and therefore brokers may be moresuccessful in matching buyers and sellers.The month in which the closing of sale occurred is included to account for variations in thereal estate market. Generally, most people start searching for houses at the end of the first13
Page 1 and 2: Real Estate Brokers: Do they inflat
Page 4: esults have risen from empirical st
Page 7 and 8: Zietz and Newsome (2001), examined
Page 9 and 10: - In terms of education he must hol
Page 11: ias, when not corrected for in the
Page 15 and 16: Other characteristics such as matri
Page 17 and 18: Table 4 │Probit estimates of brok
Page 19 and 20: This is surprising as the assisted
Page 21 and 22: We estimate separate regressions fo
Page 23 and 24: Table 5 │House price estimates: O
Page 25 and 26: experience of buyers and sellers me
Page 27 and 28: ReferencesBajtelsmit, V. L. and E.
Page 29: Annex 2 │Chart of MarseilleThe ma

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