Annual Survey of Service Industries: Real Estate Rental and Leasing and Property Management

Annual Survey of Service Industries: Real Estate Rental and Leasing and Property Management
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Detailed information for 1998

Status:

Active

Frequency:

Annual

Record number:

4705

This survey collects the financial and operating data needed to produce statistics on the Real Estate Rental and Leasing and Property Management industries in Canada.

Data release - February 01, 2001

Description

This survey collects the financial and operating data needed to produce statistics on the Real Estate Rental and Leasing and Property Management industries in Canada.This survey collects the financial and operating data needed to produce statistics on the Real Estate Rental and Leasing and Property Management industries in Canada. These data are aggregated with information from other sources to produce official estimates of national and provincial economic production in Canada. The estimates are used by government for national and regional programs and policy planning and by the private sector for industry performance measurement and market development.

Statistical activity

The survey is administered as part of the Unified Enterprise Survey program (UES). The UES program has been designed to integrate, gradually over time, the approximately 200 separate business surveys into a single master survey program. The UES aims at collecting more industry and product detail at the provincial level than was previously possible while avoiding overlap between different survey questionnaires. The redesigned business survey questionnaires have a consistent look, structure and content. The unified approach makes reporting easier for firms operating in different industries because they can provide similar information for each branch operation. This way they avoid having to respond to questionnaires that differ for each industry in terms of format, wording and even concepts.

This survey is part of the Service Industries Program. The survey data gathered are used to compile aggregate statistics for over thirty service industry groupings. Financial data, including revenue, expense and profit statistics are available for all of the surveys in the program. In addition, many compile and disseminate industry-specific information.

Reference period: Calendar year

Collection period: Beginning of January to end of June

Subjects

  • Business, consumer and property services
  • Business performance and ownership
  • Financial statements and performance
  • Rental and leasing and real estate

Data sources and methodology

Target population

The target population consists of all statistical establishments (sometimes referred to as firms or units) classified as Lessors of Real Estate (NAICS 5311), excluding Lessors of Social Housing Projects (NAICS 531112), and Real Estate Property Managers (NAICS 53131) according to the North American Industry Classification System (NAICS) during the reference year.

Sampling

This is a sample survey with a cross-sectional design.

The target population for this survey is all establishments classified to NAICS 531111 - Lessors of Residential Buildings and Dwellings (except Social Housing Projects), 531120 - Lessors of Non-Residential Buildings (except Mini-Warehouses), 531130 - Self-Storage Mini-Warehouses, 531190 - Lessors of Other Real Estate Property and 531310 - Real Estate Property Managers on Statistics Canada's Business Register (BR) and operating for at least one day during the reference year 1998. Included in the target population are those self-employed, unincorporated individuals, as reported by T1 data from Canada Customs and Revenue Agency (CCRA), who are not on the Business Register. Because no GST is paid on real estate rental, there is a substantial portion of leasing activity which is not found on Statistics Canada's Business Register.

Sample design:

A probability sample with network sampling was employed. Sampling units were created using a cell concept, which combined province and a 5 digit NAICS aggregation. Sampling units were stratified in four size strata that were defined by the total revenue of the sampling unit. For the size stratification, there is one take-all stratum for the large sampling units, two take-some strata for the medium ones (a large and a small), and one take-none stratum for the small ones. Any units in sample below the small size cut-off did not receive a questionnaire but were derived from tax. The sample size for the surveyed portion was 2,706 units and the sample size for the take none portion was 496 units.

Data sources

Responding to this survey is mandatory.

Data are collected directly from survey respondents and extracted from administrative files.

Forms were sent to all the establishments included in the sample. The survey collection that is a mail out / mail back survey, started in the spring of 1999 and ended in October 1999. Electronic reporting was also possible. The survey was also supported by Computed Assisted Telephone Interview (CATI).

View the Questionnaire(s) and reporting guide(s).

Imputation

Reported data were examined for completeness and inconsistencies using automated edits coupled with analytical review. Every effort was made to minimize the non-sampling error of omission, duplication, reporting and processing.

Partial records were imputed to make them complete, and were added to a donor pool along with completed records. Data for non-respondents, unable to locate and no-contacts were imputed using nearest neighbour donor imputation. Tax data was used in order to identify nearest neighbour donors.

Estimation

The sampling weights derived from the sample design were modified and improved using post stratification. Estimates were derived using the final weight calculated by the sample design weight multiplied by the adjustment weight. The adjusted weight is a function of the information used at the design stage, the information received from the respondent, and new information on the frame. This is possible because the Business Register was updated with more accurate information in the time between when the sample was selected and the estimates were produced. The final set of weights reflects as closely as possible the characteristics of the population in this industry.

Three sources of data were used to derive the estimates:

* a probability sample survey of real estate lessor establishments with an annual gross business revenue greater than or equal to a cut-off that varied by province from $45,844 to $163,597.

* taxation data to estimate for businesses with an annual gross business revenue found on the Business Register less than a small size cut-off that varied by province from $45,855 to $163,597.

* Taxation data to estimate for unincorporated businesses (T1) with an annual gross business revenue of more than $29,999 not found on the Business Register.


The frame for the selection of the probability sample is Statistics Canada's Business Register. For 1998, in this frame 66,588 establishments were classified to real estate leasing and property management.

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects which could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential. If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data.

Data accuracy

While Statistics Canada uses a variety of methods to ensure high standards throughout all collection and processing operations the results are always subject to a certain degree of error.

* Sampling errors can occur due to the fact that the estimates are derived from a sample of the population as opposed to the entire population. These errors depend on factors such as sample size, sampling design and the method of estimation.

* Non-sampling errors may occur for many reasons that are unrelated to sampling. Non-response and misclassification of the business (out of scope) are the most common factors. Some examples of other non-sampling errors are incorrect or incomplete information from respondents, differences in interpretation of the questions, errors in capturing, coding and processing of reported data. Every effort was made to minimize the non-sampling error of omission, duplication, reporting and processing.

Since this survey was based on probability sampling the potential for error caused by sampling can be measured. A standard measure of sampling error is the coefficient of variation (CV). The qualities of CVs are rated as follows:

* Excellent 0.01% to 4.99%
* Very good 5.00% to 9.99%
* Good 10.00% to 14.99%
* Acceptable 15.00% to 24.99%
* Use with caution 25.00% to 34.99%
* Unreliable 35.00% or higher

Documentation

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