Literature DB >> 28516142

Survey data on factors affecting negotiation of professional fees between Estate Valuers and their clients when the mortgage is financed by bank loan: A case study of mortgage valuations in Ikeja, Lagos State, Nigeria.

Chukwuemeka O Iroham1, Hilary I Okagbue2, Olalekan A Ogunkoya1, James D Owolabi3.   

Abstract

In this article, two sets of questionnaires were administered to professionals and clients (commercial banks) on their willingness to negotiate the professional fees charged by the Estate Valuers assuming that the mortgage in valuation was financed by bank loan. A range of fees options were provided. Other factors such as the business environment and mortgage valuation can influence the negotiated fees when the data obtained from the survey data is analyzed.

Entities:  

Keywords:  Bank loan; Clients; Estate Valuers; Ikeja; Mortgage valuation; Nigeria; Questionnaire

Year:  2017        PMID: 28516142      PMCID: PMC5424955          DOI: 10.1016/j.dib.2017.04.047

Source DB:  PubMed          Journal:  Data Brief        ISSN: 2352-3409


Specifications Table Value of the data Can be used for educational and research purposes and by mortgage industry. The data can provide insight on the factors responsible for the professional fees paid by clients for mortgage valuation when the properties are acquired through bank loans. The questionnaires can be adapted, adopted or modified for a similar research. The data is valuable for socioeconomic analysis of mortgage valuation and ethics in negotiation of professional fees. See [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17] for other socio-economic data. To understand the ethical practice of negotiation of professional fees within the approved standard and this can serve as basis for policy implementation by the appropriate professional and regulatory bodies.

Data

The data is a set of responses obtained from the administration of two different sets of questionnaires to Estate Valuers that deal in property valuation and their clients (commercial banks) within the Ikeja axis of Lagos State, Nigeria. The questionnaires were designed to solicit information on how much the professionals are willing to accept from their clients and also how much the clients are willing to pay assuming the property was financed through bank loan. Analysis of the data (responses from the questionnaires) can provide an insight on the various factors that can influence professional fees. The list of all the supplementary data used in this article is summarized in Table 1.
Table 1

Supplementary materials.

AppendixData
AQuestionnaire administered to the clients
BQuestionnaire administered to the professionals
CThe response obtained from the clients in SPSS text file
DThe response obtained from the professionals in SPSS text file
Supplementary materials.

Experimental design, materials and methods

The Estate Surveyors and Valuers Registration Board of Nigeria (ESVARBON) is a body that is statutorily responsible for the regulation of compensations paid by clients to professionals in the Nigerian Institution of Estate Surveyors and Valuers (NIESV). The compensations are in the form of scale upon which the agreed professional fees must be charged. However, the socio-economic realities in Nigeria have necessitated clients to negotiate the charges offered to them by the professionals. It should be noted that mortgage valuation is key to determination of professional fee. Surveys are very vital in understanding and predicting key population characteristics [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31]. In this case, Ikeja, Lagos, Nigeria was chosen for the research and the study area is indicated in Fig. 1.
Fig. 1

Map of Ikeja with the study area marked out. Source: Map – Google images/ maps [32].

Map of Ikeja with the study area marked out. Source: Map – Google images/ maps [32]. The sampling frame is summarized in Table 2.
Table 2

The sampling frame.

RespondentsSampling frame
Professionals82 Registered real estate firms according to the directory of (NIESV) [33]
Clients55 branches of commercial banks in the study area [34]
The sampling frame. The sample size is estimated as a percentage of the sample frame using the formula;where: n = sample size e = permissible error p = sample proportion q = 1-sample proportion, that is, 1− p N= sample population or sample frame. This research adopted a confidence level of 95%, a sample proportion of 0.05, an allowable error of within 5% of the true prevalence, with the sample frame for registered surveying firms as 82, and the sample frame for commercial banks as 55. The corresponding sample sizes will be calculated using the formula above. The sample size for the registered estate firms is given as: Thus a total of 35 registered Estate Surveying and Valuation Firms in the study area have been used as sample size, this represents about 42% of the total number of Estate Surveying and Valuation Firms within the sample frame. The sample size for the commercial banks in the study area is calculated as: Thus a total of 29 commercial banks in the study area were used as the sample size; this represents about 53% of the total number of Estate Surveying and Valuation Firms within the sample frame that deal in property valuation. Simple random sampling was used to administer the questionnaires to each group of respondents. The questionnaires were administered in English and the fees in Nigerian currency (Naira). The fees allowable are within the approved scale. Other factors bordering on ethics of the profession were included in the questionnaires. Details on the studied population can be accessed in [33], [34].
Subject areaEconomics
More specific subject areaMortgage Valuation.
Type of dataTables and Text files
How data was acquiredField survey
Data formatRaw
Experimental factorsSimple random sampling of Estate Valuers and their clients in Ikeja, Nigeria
Experimental featuresSample selection of views of clients and professionals on negotiated fees payable or receivable by each party as appropriate
Data source locationNigeria.
Data accessibilityAll the data are in this data article
  27 in total

1.  Survey of socio-economic and contextual factors of households׳ energy consumption.

Authors:  Omar Jridi; Fethi Zouheir Nouri
Journal:  Data Brief       Date:  2015-09-26

2.  Data of a willingness to pay survey for national climate change mitigation policies in Germany.

Authors:  Reinhard Uehleke
Journal:  Data Brief       Date:  2016-03-09

3.  The data of GDP and exchange rate used in the Balassa-Samuelson hypothesis.

Authors:  Weiguo Wang; Jing Xue; Chonghua Du
Journal:  Data Brief       Date:  2016-10-03

4.  Dataset for corporate valuation and analyses of peer effects in corporate practices and local factors favoring innovation.

Authors:  Andrea Carosi
Journal:  Data Brief       Date:  2016-12-10

5.  Newly listed firms' M&A activities data and their VC-backing data.

Authors:  Ting Cao
Journal:  Data Brief       Date:  2016-11-09

6.  Data on burden of comorbidities in the united states and medicaid expansion status.

Authors:  Tomi Akinyemiju; Justin Xavier Moore
Journal:  Data Brief       Date:  2016-05-20

7.  Measuring resilience to financial instability: A new dataset.

Authors:  Domenico Lombardi; Pierre Siklos
Journal:  Data Brief       Date:  2016-11-09

8.  Data on Vietnamese patients׳ financial burdens and risk of destitution.

Authors:  Quan-Hoang Vuong; Trong-Khang Nguyen
Journal:  Data Brief       Date:  2016-09-30

9.  Real estate market and building energy performance: Data for a mass appraisal approach.

Authors:  Pietro Bonifaci; Sergio Copiello
Journal:  Data Brief       Date:  2015-11-24

10.  Dataset for analysing the relationships among economic growth, fossil fuel and non-fossil fuel consumption.

Authors:  John Asafu-Adjaye; Dominic Byrne; Maximiliano Alvarez
Journal:  Data Brief       Date:  2016-11-26
View more
  14 in total

1.  Housing price gradient and immigrant population: Data from the Italian real estate market.

Authors:  Valentina Antoniucci; Giuliano Marella
Journal:  Data Brief       Date:  2017-12-15

2.  Survey datasets on the externalizing behaviors of primary school pupils and secondary school students in some selected schools in Ogun State, Nigeria.

Authors:  Sheila A Bishop; Enahoro A Owoloko; Hilary I Okagbue; Pelumi E Oguntunde; Oluwole A Odetunmibi; Abiodun A Opanuga
Journal:  Data Brief       Date:  2017-06-16

3.  Data from RE distressed market: Properties auctions in Italy.

Authors:  Rubina Canesi; Giuliano Marella
Journal:  Data Brief       Date:  2018-03-08

4.  Data exploration on factors that influences construction cost and time performance on construction project sites.

Authors:  Lekan M Amusan; Adedeji Afolabi; Raphael Ojelabi; Ignatius Omuh; Hilary I Okagbue
Journal:  Data Brief       Date:  2018-02-16

5.  Survey data on users perception of flexibility of spaces in selected cultural center in southwest Nigeria.

Authors:  Adedapo Oluwatayo; Adedotun O Akinola; Tosin Babalola; Hilary I Okagbue; Samuel Olademehin; Segun Eyiaro; Samuel Oludara; Ometaghogho Johnson; Oluwasina Famurewa; Obiora Obi; Adebambo Adewakun; Ekara N Ekara
Journal:  Data Brief       Date:  2018-07-02

6.  Survey dataset on occupational hazards on construction sites.

Authors:  Patience F Tunji-Olayeni; Adedeji O Afolabi; Obiora I Okpalamoka
Journal:  Data Brief       Date:  2018-04-13

7.  Statistical analysis of bank deposits dataset.

Authors:  Pelumi E Oguntunde; Hilary I Okagbue; Patience I Adamu; Omoleye A Oguntunde; Sola J Oluwatunde; Abiodun A Opanuga
Journal:  Data Brief       Date:  2018-03-26

8.  Personal name in Igbo Culture: A dataset on randomly selected personal names and their statistical analysis.

Authors:  Hilary I Okagbue; Abiodun A Opanuga; Muminu O Adamu; Paulinus O Ugwoke; Emmanuela C M Obasi; Grace A Eze
Journal:  Data Brief       Date:  2017-09-01

9.  Survey datasets on patterns of utilization of mental healthcare services among people living with mental illness.

Authors:  Tomike I Olawande; Hilary I Okagbue; Ayodele S Jegede; Patrick A Edewor; Lukman T Fasasi
Journal:  Data Brief       Date:  2018-07-05

10.  Datasets on factors influencing trading on pedestrian bridges along Ikorodu road, Lagos, Nigeria.

Authors:  Olabisi O Ajakaiye; Hammed A Afolabi; Adedotun O Akinola; Hilary I Okagbue; Omoniyi O Olagunju; Olufumilayo O Adetoro
Journal:  Data Brief       Date:  2018-06-22
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.