development and implementation of gis-based property …

African Journal of Land Policy and Geospatial Sciences, ISSN2657-2664, Vol.2, No.3, (September 2019)

97

DEVELOPMENT AND IMPLEMENTATION OF GIS-BASED PROPERTY TAX

MANAGEMENT SYSTEM FOR BENIN CITY, NIGERIA

1Balogun, 2Toju Francis, 1Dept of Geography and Regional Planning, University of Benin, [email protected], Benin City, Nigeria

ABSTRACT

The study looks at the implementation of a GIS - Based Property Tax Information

Management System to solve the problem of low internally generated revenue. It

also considers the appropriateness of high-resolution satellite image in

generating property information in the absence of land registry record. Building

characteristics needed for property valuation that could not be derived from high

resolution satellite were collected from property owners using structured

questionnaire. GPS coordinate points of sampled properties, property documents

and property owners’ photographs were hyperlink. The field-generated data,

satellite derived data and Valuation model were combined in a GIS environment

to automate property tax assessment procedure. Result indicates that Computer

Assisted Mass Appraiser (CAMA) method used in property tax determination is

efficient and effective and can greatly improve service delivery in property tax

administration.

Keywords :

Valuation model

Property tax

High resolution image

Database query

Hyperlink

Computer Assisted Mass

Appraiser

Received in : 26.12.2018

Reviewed form in: 22.08.2019

Accepted in : 05.09.2019

Published in: 30.09.2019

mailto:[email protected]

https://revues.imist.ma/index.php?journal=AJLP-GS&page=editor&op=setDueDate&path%5B%5D=14723&path%5B%5D=3610


Balogun and Toju F. / DEVELOPMENT AND IMPLEMENTATION OF GIS-BASED PROPERTY TAX

African Journal of Land Policy and Geospatial Sciences, ISSN2657-2664, Vol.2, No.3, (September 2019) 98

DÉVELOPPEMENT ET MISE EN ŒUVRE D'UN SYSTÈME DE GESTION DE

L'IMPÔT IMMOBILIER À BASE DE SIG POUR LA VILLE DE BENIN, AU

NIGÉRIA

Résumé

L'étude examine la mise en œuvre d'un système de gestion de l'information sur les taxes foncières basé sur un SIG afin de résoudre le problème des faibles revenus générés en interne. Il examine également la pertinence d'une image satellite à haute résolution pour générer des informations sur les biens en l'absence de registre foncier. Les caractéristiques des bâtiments nécessaires à l'évaluation des propriétés qui ne pouvaient pas être dérivées d'un satellite à haute résolution ont été collectées auprès des propriétaires à l'aide d'un questionnaire structuré. Les coordonnées GPS des propriétés échantillonnées, des documents immobiliers et des photographies des propriétaires étaient des hyperliens. Les données générées sur le terrain, les données dérivées par satellite et le modèle d'évaluation ont été combinés dans un environnement SIG pour automatiser la procédure d'évaluation de l'impôt foncier. Le résultat indique que la méthode de l’évaluateur de masse assisté par ordinateur (CAMA) utilisée dans la détermination de l’impôt foncier est efficace et peut considérablement améliorer la prestation de services en matière d’administration de l’impôt foncier.

Mots clés :

Modèle d'évaluation

Taxe foncière

Image haute résolution

Requête dans une base de

données

Lien hypertexte

Evaluateur de masse assisté par

ordinateur

Reçu le : 26.12.2018

Evalué le : 22.08.2019

Accepté le : 05.09.2019

Publié le: 30.09.2019





1. INTRODUCTION

Property tax is one of the approved sources of income for municipal funding in Nigeria. Several nations

of the world are utilizing property tax as a means of financing municipal social infrastructure

(Dillinger, 1993). Though property tax has been considered administratively difficult, and expensive,

slow in process, politically unpopular, and hard to manage in high inflation conditions (Mou, 1996,

Dellinger, 1999 and Coker, 2001) yet this source of revenue has been said to be the most lucrative and

promising (Kariuki, Nzioki and Murigu, 2009). This notwithstanding, property tax, according to Kelly

(2001) is the least tapped source of tax revenue to support urban governments in Africa. Heavy

reliance on manual method of filling, recording, storing and retrieval of information and valuation has

been identified as the major cause of low buoyancy in Africa. In manual method, there is no link

between the location of a property and its corresponding data, thus rendering all forms of spatial

analysis impossible. Manual method has been found to be ineffective and inefficient because it is time

consuming, costly, cumbersome and fraud laden.

Property tax base usually covers a large number of tax objects, and requires extensive, ever changing

information on each property (Kelly, 1999). Hence, property tax administration requires a dynamic

system that is adaptable to such ever changing information and is able to handle large data. This makes

GIS an ideal technology for property tax management. Successfully utilizing computerization depends

on the ability to link the data-processing activities with the administrative components of property

taxation. These two components must be effectively integrated to form a comprehensive property tax

management system. This helps to achieve total transparency as spatial dimensions of all the

structures can effectively be maintained in a GIS environment (Mantey and Tagoe, 2012). In

operational GIS all taxable units are identified and billing and monitoring of the revenue collection

operations are organized. According to Droj, Droj and Mancia (2010) building and maintaining the

property inventory and attribute database are the most labor-intensive and hence most expensive

aspects of property valuation and property tax administration. Once this is well design and carried out

others becomes easier.

The adoption and implementation of digital formats in the management of land records in Nigeria is

fundamentally important for effective and efficient land administration practice and sustainable

development (Akeh, Butu and Modu, 2014). Property tax law known as Land Use Charge Law in Edo

State was introduced in Benin City in 2012 with the aim of improving Internally Generated Revenue

(IGR) for the purpose of providing facilities and services. The procedure currently in use in Edo State

Land use Charge is manual and is not yielding expected revenue just as with other property tax

agencies in Nigeria. Manual method with all its attendance challenges is incapable of handling large

database like property tax. Since the purpose of property tax information management systems is to

support the administration of property taxation, if the tax details are maintained electronically and

geo-spatially, the efficiency of tax collection will be improved. It is on this basis that this paper looks at

the development of GIS-based property tax management system for Benin City. It also examines

alternative means of generating timely property information where cadastral map is absent.



2. METHODS

2.1.1. Study Area

Benin City, the capital of Edo State is located in Southern Nigeria. The geographical co-ordinates of the

city limits lie within Latitudes 6°261 and 6°341 North of Equator and Longitude 5°351and 5°41 East of

Greenwich Meridian. Benin City has a projected population of 1,682,158. Uwasota and Edo

Development property Authority neighbourhoods were chosen for detailed study.

2.1.2. Data Acquisition

Spatial and non-spatial data required for property tax administration were collected for database

design. The spatial datasets describe those objects that are linked to specific locations such as parcels,

buildings, roads and streets. The non-spatial data includes the attribute information that describes the

spatial data such as the property address, property ownership, occupant, taxpayer, property use and

building type. Since cadastral map, which is usually the source data for property information system, is

not available in the Survey and Mapping unit of the Ministry of Lands and Survey, Edo State, Ikono

multispectral image of 3.28 meters (multispectral) resolution at Nadir for year 2013 covering the

study area was used as an alternative to cadastral map. This high-resolution satellite image provided

the spatial information required for the calculation of property tax.

The satellite image was geo-referenced and on-screen digitizing of buildings footprints, zones, roads

and streets was carried out to create a map that shows all existing buildings as at 2013. The digitizing

process automatically assigns unique identifier to each feature digitized. This uniqueness provides

users the capabilities of exploiting the attribute information intrinsic to tax assessment. The process

generated the locations of property boundaries, areas of properties in the squared meter required in

the calculation of property tax and also relatively up-to-date record of all the properties in the study

area. Editing of the digitized work was done to correct any error that might have been introduced

during the process of digitizing such as closing building polygon footprint where it does not close. The

ground truthing was carried out to check and correct any object that has been wrongly categorized.

2.1.3. Tax Base

The property tax base is the extent of coverage of the taxable property. The manual method presently

used by Land Use Charge Department to generate property information cannot provide all the needed

information about the taxable properties this usually results into loss of revenue and low tax yield.

This low buoyancy is one of the major challenges of property tax management in Nigeria. The use of

high-resolution satellite image provided solution to this challenge. While it helped to achieve 100

percent coverage which is key to tax buoyancy, property classification through field survey aided in

bringing in the taxable property into tax base. Manual method of property assessment cannot achieve

this feat because of error of omission or commission.



2.1.4. Property Survey

Structured questionnaire designed for the property enumeration was administered to the property

owners or their representatives. The study area has 3333 buildings that were digitized (Figure 1). Out

of these, 10% which

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Minor Roads

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Boundary of Study Area

Figure 1 Properties in the study area

is 333 buildings were selected for survey. While administering questionnaire to the property owner,

his/her property was also assessed. The attribute information or building characteristics needed for

property valuation that could not be derived from the image were collected from the field during the

administration of questionnaire. This includes owners’ name, house number, address, building type,

zone, age, use, tenancy, connection to electricity and availability of water. The quality and quantity of

the data used, according to Gloudemans (1999), correlates with the accuracy of the resulting

assessments; hence care was taken to ensure accuracy and correctness. Although the digitized

buildings had unique identification attached automatically during the process of digitizing but to

differentiate the sampled properties from other properties in the study area, the GPS coordinate points

(Figure 2) of the sampled properties were recorded during the property enumeration.



Figure 2: location of sampled properties

This was done to make the data entry of sampled properties easy and to permit joint operation. Where

the property owners were willing, the photocopy of properties documents, such as survey plan and

approved building plan, were collected for scanning. The photographs of the property owners and

their properties were also taken. These were scanned into the database and used for HyperText

Markup Language (HTML) and hyperlink. Property map was spatially joined with property attributes

in order to assist in property tax determination. This integration of attribute data and spatial

information forms the geo-database. Geo-database was combined with tax model estimator to provide

simple and effective taxation system.

2.1.5. Property tax valuation model development

Modeling an estimator for property tax management was developed using the valuation method.

Property valuation is a complex exercise. Providing property owners with an easily understood

explanation of how their properties have been valued remains a continual challenge to the assessors.

For example, the same building type can be valued differently depending on the use or location. Edo

State adapted Lagos State model for property tax valuation which the staff of Edo Land Use Charge

observed that operationalizing it was problematic. Hence, simple, yet effective model was developed.

According to Yomralioglu and Nisanci, (2004) there are as many as 28 variables that could be used for

property valuation. However, Turnquist, (2006) points out that we do not get effective models by

including every detail of every action that occurs in a system. Thus effective modeling forces us to be

selective in what we include, to eliminate unimportant details and to emphasize important



relationships. With this philosophical base Turnquist (2006) postulates four main characteristics that

are important for effective modeling.

These are that an effective model:

i). Focuses on producing an output someone wants, and knows how to use.

ii). Includes the important variables that describe how the system works and represents their

interactions clearly and correctly.

iii). Operates in a way that is verifiable and understandable and

iv). Is based on data that can be provided, so that it can be calibrated and tested.

This informs the reason for the development of a simpler model which can easily be understood. The

model was created on an area-based approach. The area-based system offers a good basis for levying a

property tax that fairly approximates market values, provided that the demarcation of fiscal zones and

the level of unit taxes are kept updated to reflect important technical characteristics of improvements

and the apparent real estate value zones of the city (Bahl, 2009). It is very useful where there is

shortage of Estate Valuer like Benin City. Additionally, area base approach is amenable to GIS when

combined with Normal Least Square Method for mass valuation. Normal Least Square Method is a

spatial statistics that could be utilized for spatial regression in a GIS environment. When property

model is run with Ordinary Least Analysis, it speeds up the valuation process and reduces cost (Eboy

and Samat, 2014). The property tax model

developed for this work is as follows:

PT = A x R x L x Ag x U x O

Where PT = Property tax

A = Area of Property in square meter

R = Rate per square meter

L = Location factor (Zone)

Ag = Age factor

U = Use factor

O = Occupancy factor

Property tax = Area × Rate × Location factor × Age factor × Use factor × Occupancy factor

Property tax is the dependent variable which is unknown. The independent variables (the predictor)

were derived from the following sources: (1) property enumeration (2) digitized map (3) land use

charge document. Each of these variables was weighted. The result determines the tax due for the

property owners to pay. Since it is ordinary Least Square Analysis, other independent variables can be

added as deemed fit by the municipal government.

2.2. Model Variable Selection and Computer Assisted Mass Appraiser (CAMA) with ArcGIS

This is a statistical technique used to analyze data in order to estimate the value of one variable

(market value) from the known value of other variables such as building size, quality, location, lot size

etc. Property tax items assessed in the field were assigned to their appropriate classes, uses and zones

to enable the system compute their respective payable revenue.



2.2.1. Area

The area or size of the property was automatically generated during the digitizing process because the

image was geo-referenced. The area of the property was rounded up to whole numbers. Rate per

square meter for each use category that was given by the Land Use Charge Law was adopted.

2.2.2. Location (Fiscal Zoning)

In land valuation, the value and potential of a property are fundamentally determined by the location

(Droj et al 2010). Consequent upon this the study area was divided into zones. There are two types of

zoning: the functional zoning and the fiscal zoning. The functional zoning is used to differentiate

sections that are devoted to different purposes such as residential, commercial, industrial etc. The

fiscal zoning is done for the purpose of property tax. In this study, the fiscal zone was demarcated in

such a way that each zone provides a homogeneous pattern of land use and housing typology. The

boundaries of the zones were set in a way that the land use patterns within the zones are roughly

similar in terms of social and economic activities. Properties sharing the same characteristics were

grouped together in the same zone. This was done to aid mass assessment and to ease the work of

assessors. The study area was divided into four fiscal zones based on land value and the amount for

each zone was assigned to sampled properties according to their grouping.

2.2.3. Age Factor

Age factor has to do with the age of each property. It was assumed that properties depreciate as they

get older. The properties were categorized into three and were assigned to different rating. These rates

were adopted for this study and assigned to the assessed buildings based on the year in which they

were built. The age of the properties were acquired during field property survey.

2.2.4. Use Factor

The use of each property varies and commands different rating. The rating was extracted from Edo

State Land Use Charge Law. The property were differentiated and assigned their rates according to the

use to which they were classified.

2.2.5. Occupancy Factor

Occupancy factor was categorized into five and assign different rating. (i) Commercial (ii) Rented

Residential (iii) Owner occupier Residential/commercial (iv) Retired owner occupier Residential (v)

Family compound. The occupancy ratings were assigned to the assessed properties according to their

occupancy factor.

The classes of variables were substituted with the rates according to the characteristics and location

of the properties. After the creation of the database information about properties were fed into the

database. ArcGIS calculator was used to run the model estimator. Result generated indicates property

tax due for each property. Updating of model may be required with time depending on what additional

variables the government feels like adding to the formulation. In other words the formula is

extendable.



3. RESULT AND DISCUSSION

3.1. Digital property tax map

Data fusion resulted into digital property tax map. Spatial analysis of the fused datasets was used to

generate a series of spatially continuous surfaces of mean value per square metre (Figure 3) for each

property class, based on the assumption of a direct linear relationship between property values and

plot size, location, age and occupancy factor. This property tax surface map (Figure 3) shows the areas

having the same property tax value. Area showing red colour indicates areas that are contributing

highest property values in the study area. This type of map can serve as a good planning tool.

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Property TaxMap in Naira

<15,000 - 20,000

21,000 - 73,000

74,000 - 170,000

180,000 - 310,000

320,000 - >620,000

Coordinate System: WGS 1984 UTM Zone 32N

Projection: Transverse MercatorDatum: Minna

False Easting: 500,000.0000False Northing: 0.0000

Central Meridian: 3.0000Scale Factor: 0.9996

Latitude Of Origin: 0.0000Units: Meter

Figure 3: Continuous surface map showing distribution of properties by mean value per

square metre

Figure 4 is an overlay of the street map of the study area and sampled property on the property

surface map for easy property location.



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<25.000 - 20.000

21.000 - 73.000

prop_tax_per annum

<NAIRA>

Figure 4: Overlay of streets and sampled properties on property tax surface map Database

Query

The database created contains the property owners name, type of property, use of property, nature of

tenancy, address of property, zone in which property is located, size of property, facilities in the

property, materials used in building the property, property finishing, and supporting documents of

property. The table also shows the quantification of the variables per property and the assessment of

each property and the tax per property. It also shows the total potential tax for the study area. The

model provides uniformity and consistency in valuations of large number of parcels at the same time.

The capability of the model to carry out mass evaluation proved that this approach could potentially

help Edo Land Use Charge Department to speed up evaluation process and reduce cost. Several

queries could be carried out on the digital property tax map. Property could be queried to know the

amount of tax due for payment, the ownership of property, the plot size, connection to electricity or

water, the zone it belongs, etc. Each of the property has an identification code that facilitates the

process of crosschecking defaulters in tax payment. With such database it is possible to know the total

properties, the exempted properties and estimate expected revenue for the current year. Figure 5 is

the screen snap shot of the database created for the property tax assessment. The database has the

capability of being used to easily and accurately capture, edit, store, retrieve, update, query,

manipulate, analyze, display, and output property data.



Figure 5: Screen snap shot of part of the Database

Properties highlighted on the table are also highlighted on the map. Identity tool can be used to get the

details of each of the properties. The box at the right side of Figure 6 shows the details of the identified

property. Scrolling down will reveal other hidden details of the property.

Figure 6: The highlighted row shows the result of query by attribute.

The database can also be queried using structured query language. For example ‘select from sampled

property where “use” is equal to residential’. Result of query by attribute is highlighted both on the

map as well as on the database as shown in Figure 7.

Figure 7: Result of query by attribute on the zone map



3.2. Hyperlink

Hyperlink is another easy way of property identification. As you move your mouse over each of the

properties, it links the property to the name of the owner in the database and display it on the screen

as demonstrated in Figure 8. This method can also be used to link the amount paid or owed by

property owners. This method reduces search time and makes the work of property tax manager

simpler and easier.

Figure 8: Hyper-linking of properties.

3.3. Linking of property Documents with Properties using HyperText Markup Language (HTML)

Property documents were linked to the properties through HTML such that when a property is

queried, a link will pop up to show the property documents. Figures 9 and 10 show the screen shot of

HTML of property that was queried for ownership and documents of property. With query, it is

possible to see the photograph of each property or property owners, know the property that has

approved plan, survey plan, C of O and any other documents that may be required that is in the

database.

Figure 9: Query with HTML popup to view the photograph of one of the properties.



Figure 10: Query with HTML popup to view property survey site plan.

Other important benefits of this study is that it provided the means of blocking the loop holes with

which the public usually defraud the government by under- declaring or falsely declaring low market

value areas as the location of their inherited (transferred) property in other to attract low valuation.

With GIS-based property information system query will bring out information concerning such

property. With this, the official can have correct information to assess the property. The database

makes property market viable as it becomes easy for seller to trade with their properties since buyers

can easily crosscheck particulars of properties. Delay in document verification is eliminated. Since

documents and ownership of property can easily be verified from the GIS-Based Information System

database property owners can use their properties as collateral to obtain loan from the bank. In the

same vein, it will solve the peculiar problem of “this house is not for sale’’ which is commonly written

on buildings in our cities in the southern part of Nigeria. This is usually written to forestall the dubious

attempts of children selling their parents’ property without their knowledge. If the prospective buyer

goes to this suggested Edo State GIS-Based Information System to verify he/she will find out that the

photograph in the database is different from that of the person posing as the owner/seller. Water-

board and electricity distributors and waste managers will find the database very useful in monitoring

and collection of bills. Courier services and post office will also find the database very useful in the

distribution of parcels.

3.4. Model Assessment

It is not enough to have a comprehensive tax system. A good information system must be able to

identify, assess and document taxable properties in an efficient way to enhance service delivery. It

must be seen to be performing better than the manual method currently being used. To evaluate the

efficiency of this system, the potential tax of the study area is compared with the amount collected by

Land use Charge Department from the areas that has already been manually assessed. The potential

tax for the sampled properties is N4, 101,176.00 per annum. Since sampled area is 10% of the study

area, the potential tax for the study area is N4, 101,176.00 x 10 = N41, 011,760.00. The amount

generated by Land use Charge Department between January and May 2014 is N57, 477.60 for the areas

that have been manually assessed [Balogun, 2016]. Two things are to be noted here. One, the area

manually assessed by Land use Charge Department is far bigger and larger than the study area. Two,



the areas manually assessed by the Land Use Charge Department are majorly commercial corridors.

The study area is not as commercialized as the area that has already been manually assessed by Land

use Charge Department. Thus if the model was used the collection would have been greater.

Another approach used in the assessment is by relating the potential tax from the study area to the

total area of Benin City. The total area of Benin City divided by the total area of the study area

multiplied by the potential tax from the study area will give us the potential property tax for Benin

City. The total area of the study area is 6.3km2. The tax for the sampled properties is N4, 101,176.00.

The potential tax for the sampled area is N4101176.00 x 10 = N41, 011,760.00. The total built up area

of Benin City is 607.5 km2 (Balogun, 2016). The total area of Benin City divided by the area of the

study area is equal to 96.4km2. Potential tax from the study area is equal to N41, 011,760.00 multiply

by 96.4 will give us ±N3, 953,533,664.00 for the whole of Benin per annum. This is the potential

property tax for Benin City. It is believed that the potential tax for Benin City will actually be more than

this because the highly commercialized and industrialized areas which attract higher taxes are not in

the study area. This amount is about 21 percent of the grand total of all the IGR for year 2013 (year of

the image used) which was N18, 899, 322,710.47.

The major shortcoming of the system is that a user friendly interface is required for those who are not

familiar with ArcGIS software. In addition, an accounting system where client can make payment and

receive bill is needed. Once these components are included, the system can be ported into the internet

for public use.

4. CONCLUSION AND RECOMMENDATION

Application of geospatial technology has the capacity not only to be used to generate property data for

property administration in the absence of cadastral map, but also has the ability to be used to bring

the tax base to 100%, remove delay in property valuation and increase property tax revenue

generation. The study also showed that GIS-based property tax system has the capability to estimate

large scale property value in an area within a short time, improves efficiency, remove delay in

valuation, achieve effective service delivery and reduce cost of operations. While recommending GIS-

based property tax information system, institutional capacity building is essential for effective

property tax service delivery especially in Remote Sensing, GIS and ICT. Many streets have no name

and many houses are not numbered especially in the developing areas of the city. It is recommended

that streets should be named and houses numbered as part of the preparation for a GIS-based property

information system.

The success of property tax administration depends on the political will of the government in power.

Governments are traditionally interested in programs that will generate revenue to run the affairs of

the state; therefore, they are mostly interested in projects that will yield immediate result. Depending

on the size of the local government, the gestation period of GIS based property tax information system

is 3 to 5 years (Nieminen, 2002). This span extends beyond the usual four year term for which a

political party is elected to govern in Nigeria. It therefore requires a government with the political will

to invest on a long-time project or one that is ready to heavily invest on a project in order to get it

completed during his term in power. Given the importance, the advantages as well as the urgency of



having a GIS based property tax information system put in place, it is recommended that government

should have the political will to see project completed on a priority basis in order to avoid midway

project failure.

5. Acknowledgment

The author is grateful to University of Benin for sponsoring the research and to Professors A. G.

Onokerhoraye, P. A. O. Odjugo, M. O. Asikhia, Professor (Mrs.) Ezemonye all of University of Benin and

Henry Okeke of Obafemi Awolowo University, Ile Ife, for their contributions towards the success of the

paper.

6. REFERENCES

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Information System: An Imperative for Efficient Land Administration in Nigeria. Paper presented at

the 4th National Conference on Issues and Trends in Environmental Sustainability Organized by the

School of Environmental studies, Federal Polytechnic Nasarawa, Nasarawa state, Nigeria, 5th –

7th November, 2014

Balogun, T. F (2016) Property Information Management for Public Infrastructure Financing in Benin

Metropolis, Edo State. Unpublished PhD Degree thesis. University of Benin, Benin City, Nigeria.

Bahl, R. (2009). Property Tax Reform in Developing and Transition Countries; USAID 2009.

Corker, L & Nieminen, J. (2001) Improving Municipal Cash Flow - Systematic Land Information

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7. Key terms and definitions

Computer Assisted Mass Appraiser: An automated method of effectively valuing a large number of

properties in a uniform and equitable way.

Database query: Means of retrieving data electronically from the database

High resolution image: an image with higher details

Hyperlink: a link that points or provides access to an element within a document

Valuation model: a set of assumptions used to determine the value of a property.

https://www.fig.net/resources/proceedings/fig_proceedings/fig2012/papers/ts05g/TS05G_mantey_tagoe_5882.pdf

https://www.fig.net/resources/proceedings/fig_proceedings/fig2012/papers/ts05g/TS05G_mantey_tagoe_5882.pdf