assessment of stat class applicability for building a ... documents/statewide...statewide land use -...

Lane Council of Governments - 859 Willamette Street, Suite 500 - Eugene, Oregon 97401-2910

Statewide Land Use - Stat Class Assessment - Report - FINAL.docx 1

Assessment of Stat Class Applicability for Building a Statewide Land Use Data Layer

Final Report

The following is a description of work performed in June 2015 by Lane Council of Governments (LCOG)

with funding from the Oregon Department of Land Conservation and Development (DLCD).

Summary This project entailed an assessment of the consistency of county assessor’s stat/building class data for

use as an input to a statewide land use data layer for the state of Oregon. If this could be confirmed to

be a viable and cost-effective option, it would provide valuable insight that could guide future work on

the development of a useful and sustainable statewide land use layer. This project attempts to provide

that confirmation.

The assessment was done in three stages; Acquisition, Comparison, and Mapping. Each stage focused on

a smaller number of counties than the one before, but in more detail. In the acquisition phase, data was

collected for a small, but representative, set of counties from five distinct regions of Oregon. Web sites

for 30 of 36 counties were found and searched for relevant information. This allowed confirmation that

stat class data is indeed widely used. Of the 27 counties currently confirmed to have stat class data, staff

acquired and compared the stat class code tables for 15 counties and also the taxlot GIS data and

improvement tables to be able to map 5 of these counties, one from each region.

In the comparison phase, the stat class code definitions were compared and commonalities were

identified. Three different common variations on how stat class data is organized across the counties

were found as well as several counties using unique schemes. Despite apparent differences in code

descriptions across the counties, the uses being captured were mostly quite similar. To determine if the

sort of categories typically needed in land use data were present, a generalized land use scheme was

developed based on the data and on LCOG staff experience with land use data uses. Application of this

scheme back to the stat class tables for the counties was found to be feasible and revealed significant

commonality among the major categories across the counties.

In the mapping phase, GIS was used to apply the general land use scheme back to improvement data for

each county. This classification was mapped to taxlots. The results were examined at several scales and

different development intensities (countywide, urban, transitional, and rural). Recognizable land use

patterns were generally seen, confirming the feasibility of the approach (Figure 1, Figure 2, see

Appendix A for more examples).


Figure 1: Land use map based on stat class

Figure 2: Air Photos (Source: Esri) for the same area


Some limitations of the stat class data were also revealed in the mapping phase that were not apparent

from the code comparison. These limitations do not appear to severely limit the usefulness of the data.

However, the focus of this assessment was on the prevalence, availability, and consistency of the stat

class data, not on its quality as a source of land use information. Further work will need to be done to

confirm that this source is of sufficient quality for intended uses.

Although there is not a strong, common standard in stat class coding across the State, it appears that

functionally similar stat class codes are common. Given a fairly simple, generalized land use coding

scheme, it was not difficult to assign land use codes to stat class codes. Overall, a common scheme of

statewide land use codes based on stat class seems very feasible.

In order to establish an ongoing program to collect this data, there are some hurdles to get over when it

comes to data availability, particularly of the improvement data, which does not seem to be covered

under current data sharing agreements (e.g. ORMAP) between most of Oregon’s 36 counties and the

State.

A number of next steps are possible that could expand the approach studied here. A first step might be

to develop a plan to expand this collection to all counties, supplement it with additional data (e.g. prop

class), and validate it with other local data and local knowledge. Once an acceptable layer is derived, a

program could be developed to maintain it over time. Given the amount achieved in this pilot project

with only a small budget and timeframe, an annually maintained, statewide land use layer seems very

attainable.

Assessment of Stat Class Applicability for Building a Statewide Land Use Data Layer – Final Report


Background The need for a statewide land use data layer for the state of Oregon has been identified at both state

and local levels. The Oregon GIS Framework identifies Land Use/Land Cover as one of the key

Framework Elements. So far, significant work to develop a standard and a statewide dataset has only

occurred on the land cover side. In order to develop a general-purpose descriptive land use model with

both statewide and local usefulness, issues regarding definitions, data model, coding scheme, data

sources, and the maintenance process will need to be addressed.

A general-purpose descriptive land use model with both statewide and local usefulness would need to

be able to be developed and maintained in a cost-effective way. Local jurisdictions have the benefit of a

great deal of local knowledge, but often lack the resources to develop and maintain data. A statewide

land use model would need to be based on commonly available data and require as little effort as

possible to assemble and maintain. It would also need to be simple enough to be easily integrated

across jurisdictions, but detailed enough to be useful for diverse tasks.

Prior to beginning this project, two promising potential data sources were briefly considered. These are

as follows:

County Assessor’s Prop Class

County Assessor’s Stat/Building Class

The comparative advantages and disadvantages of prop class and stat class were explored. They were

seen to be as follows.

Prop Class

Advantages

Standardized statewide

Legally mandated statewide

Currently centrally collected via ORMAP/DOR (available to DLCD through request to GEO)

Covers all parcels, improved or not

Disadvantages

Highest and best use (HBU), rather than existing use

Unknown aggregation rules (one-to-many problem for taxlots with multiple improvements)

Too few categories (e.g. residential is not broken down into single family and multi-family)

Tied to zoning (not an independent land use measure)

Not all counties participating in ORMAP (6 are not, as of 2015)

Stat Class

Advantages

Existing use or structure type



Required, in some form, by assessment best practices

Only covers improved parcels

Disadvantages

May not be standardized

May not differentiate structure type from use (e.g. house converted to commercial use)

Requires aggregation rules and processes to map to parcels (one-to-many problem)

Not currently centrally collected

Ease of acquisition may vary; may be costs associated in some counties

While prop class may play a role in refining a statewide land use layer, its disadvantages suggest it to be

(at least by itself) inadequate for the purposes anticipated for the land use layer.



Objective The main goal of this assessment was to compare county Assessors’ stat class or building class code

tables for multiple counties and to evaluate the consistency among them, as well as their suitability for

deriving a simple, common set of useful land use codes.

Although it would have been ideal to collect data for all Oregon counties, this was not feasible in the

context of this initial pilot assessment. Instead, LCOG staff collected tabular data for a small, but

representative, set of counties. These 15 counties were chosen as representing different geographic

regions of the state, the Portland Metro area, Northwestern Oregon (the Willamette Valley and

Northern Oregon Coast, excluding the Portland Metro area), Southern Oregon, Central Oregon, and

Eastern Oregon (Figure 3).

Within those regions, as much diversity of economic and geographic character as possible was sought to

maximize the differences. The intent of this is to create a sort of “worst case scenario” within the

sample. What was hoped for was to capture the most possible differences in local land use conditions

and, thus, in the codes in use.

Figure 3: Regions of the state (for the purposes of this assessment)

Tasks The following tasks were proposed to be performed in the scope of work for this project.

Assess availability and acquire assessor stat class (building) code tables for selected

representative Oregon counties (approximately 10).

Assess availability and acquire GIS tax lot and improvement data, including the stat class

attribute, for 3 or more counties selected to be representative of the geographic and

development diversity of Oregon counties.

Assess the commonalities and differences among the stat class codes for each county.



Assess the feasibility of deriving a simple land use coding scheme based on the common

categories found in the assessor classifications.

Create sample maps depicting a small number of rural, transitional, and urban areas in three

counties using the stat class attribute applied to the tax lot data. The stat class layer will also be

compared to other relevant themes (e.g., aerial imagery, land cover, zoning) for the same areas.

Describe the findings of the above work.

Propose a set of next steps to develop a statewide land use layer based on project findings.

Deliverables A project summary document (this document), including the following.

o Findings of the stat class assessment.

o A summary of the coding scheme derived from the common categories found in the stat

class tables from each county.

o Proposed next steps.

The project summary document (this document) also includes appendices, such as the following.

o A set of sample maps for the selected project areas.

o The stat class tables from each of the project counties, and the corresponding general

land use groups assigned to them for comparison.

Regular communications to monitor project progress and a minimum of one on-site meeting

with DLCD staff to review a draft of the project findings summary, discuss the results and

proposed next steps.

All data collected for the purposes of this assessment (delivered separately in digital format).



Methods This assessment was done in three stages. Each stage focused on a smaller number of counties than the

one before, but in more detail. The detailed steps involved in the stages are as follows.

Web Search and Acquisition Conducted a web search for county assessor web sites or GIS department web sites for all

counties in Oregon.

o Looked for sites that gave either access to reports showing the stat class codes or

downloads of one or more of the following.

The stat class code definitions (usually from the Assessor).

The improvements table (usually from the Assessor).

The taxlots GIS layer (usually from the GIS department).

o Downloaded what data was available off the web sites.

o Recorded contact information and the status of the county in terms of whether they

appear to have a stat class field on their data and what it is called.

Contacted select counties not represented by the above data collection.

o Focus was on filling in the underrepresented areas of the state.

Code Assessment Compiled the stat class code description tables for all available counties (15) into a single table

for comparison.

o Looked for patterns, clusters of similar codes or numbering sequences.

Developed a simple, generalized land use group coding scheme which could be applied to the

codes from each county.

o Applied the generalized land use group coding to each county stat class code table.

Map Assessment Created maps of the stat-class-based land use groups applied to the taxlots for each county.

o Mapped several different areas of each county.

Assessed the maps for basic issues with the land use coding scheme.



Findings

Web Search and Acquisition The first phase of this assessment was a web search for county assessor web sites or GIS department

web sites for all counties in Oregon. LCOG staff looked for sites that give either access to reports

showing the stat class codes or downloads of one or more of the following.

The stat class code definitions (usually from the Assessor).

The improvements table (usually from the Assessor).

The taxlots GIS layer (usually from the GIS department).

Staff downloaded what data was available off the web sites and also recorded contact information and

the status of the county in terms of whether they appear to have a stat class field on their data and what

it is called.

So far as LCOG staff were able to ascertain, 30 of the 36 Oregon counties make assessment data

available online in one form or another. We have reviewed each of these counties with online data. In

some cases we supplemented this review with direct contact that confirmed additional counties. Staff

have confirmed that 27 counties have stat class (or bldg class/imp type) data in one form or another.

Of the 27 counties currently confirmed to have stat class data, staff acquired the stat class code tables

for 15 counties (5 more than originally planned) and also the taxlot GIS data and improvement tables to

be able to map 6 of these, of which 5 regionally representative counties were chosen for the maps in

this report. The following table (Figure 4) describes the counties for which staff collected data and which

were compared, as well as the subset of those that were mapped, by region.

Region Mapped Compared

Central Deschutes Deschutes

Crook

Lake

Eastern Harney Harney

Wheeler

Baker

Metro Multnomah Multnomah

Southern Josephine Josephine

Douglas

Jackson

Coos

Northwest Lane Lane

Columbia

Marion

Polk

Figure 4: List of counties used for mapping and code comparison

The following map (Figure 5) also depicts this. The full inventory of all counties, including other details, is

included as Appendix B.



Figure 5: Assessment status of counties

During this phase, LCOG staff also contacted the Oregon Department of Revenue (DOR) and the Oregon

Geospatial Enterprise Office (GEO) to confirm that this data was not already centrally collected. It is not.

Although the ORMAP program collects taxlot data from all but a few counties in the state, the state

cadastral data standard only requires prop class codes be included. Improvement tables from the

assessors typically include the stat class attributes, sometimes assigned to more than one improvement

per taxlot, and these are not shared with DOR. GEO also does not independently collect this

information.

This acquisition phase allowed staff to determine that stat class data is widely used and that code tables

are sometimes available online. Many counties do make at least some data available online, usually

without cost.1 Most of the same counties that currently do not participate in ORMAP also do not make

data available online. The availability and format of data also varies. Microsoft Access databases, ESRI

file geodatabases, ESRI shapefiles, and Adobe Acrobat PDF files were all common. Some code tables

were in PDF format in such a way that text could only be extracted using optical character recognition

(OCR) software. Other formats were more accessible.

Code Assessment For the code assessment phase, staff compiled the stat class code description tables for all available

counties (15) into a single table for comparison. This table is included as Appendix C. Once the data was

in this format, it was possible to look for patterns and clusters of similar codes or numbering sequences.

1 One county, Wasco, asked for a small fee ($40) to cover administrative costs. The data for that county was not

acquired for this study as it was not needed for the representative sample (LCOG had already acquired data for several other Central Oregon counties).



Based on conversations with Lane and Polk County Assessors prior to beginning the project, the data to

be assessed was believed to be available in all counties in Oregon and was believed to be fairly

consistent, regularly maintained, and of adequate quality.

LCOG staff found several different common variations on how stat class data is organized across the

counties. As might be expected, a few counties appear to use coding schemes that are unique to them.

It was apparent from similarities in samples of the data available on the internet that similarities in data

partly spring from the fact that the same assessment records management software vendors (e.g.

Helion) are being used in multiple counties. The difference counts of field names used (Figure 6) gives a

sense of the pattern. For the counties for which staff acquired data, coding schemes were found to be of

three basic types (Figure 7).

Column Name

No. of Counties

STAT CLASS 12

BLDG CLASS 6

IMP TYPE 6

PROP CODE 1

Unknown 2

Unconfirmed 9

Figure 6: Column names found

TYPE 1 TYPE 2 TYPE 3 UNIQUE

Deschutes Crook Baker Multnomah

Harney Lake Josephine Douglas

Jackson Wheeler

Lane

Coos

Marion

Columbia Polk

Figure 7: Types of similar stat class schemes found

While it would be possible to compile extensive comparative statistics on these counties, these would

not be very useful. A more productive way of comparing them is to see if a basic set of common land use

categories are present for all. To do this, LCOG staff used the existing grouping categories present in

about half the counties, our own experience with land use data use cases, and a code-by-code and

county-by-county tuning to find the best fit in context, to develop a set of basic categories (Figure 8).

These codes were then assigned back to all stat class codes in each county so that all counties had a

lookup table in this common scheme that could be applied in the mapping process. The lookup tables

for each county can be found as Appendix D.



Land Use Group Definition

COMMERCIAL Primary improvement with commercial function, including retail, wholesale, professional services, and financial

COMMUNITY Primary improvement with community function, including government, public safety, education, recreation, or major health care facilities

INDUSTRIAL Primary improvement with industrial function, including durable and non-durable goods manufacturing and warehouse storage

MISCELLANEOUS Primary improvement with miscellaneous functions that do not fit in any other category or span categories; usually auxiliary structures

MIXED USE Primary improvement with mixed use function, containing both commercial and residential uses

MULTI-FAMILY RESIDENTIAL Primary improvement with residential function, multi-family structures or multiple structures per taxlot, including duplexes, triplexes, fourplexes, and mobile home parks

RESOURCE Primary improvement with agricultural, forestry, or other natural resource extraction related functions (not including manufacturing of related products)

SINGLE FAMILY RESIDENTIAL Primary improvement with residential function, single family structures, detached or attached, typically one structure per taxlot, including condos, townhouses, and manufactured homes on a lot

VACANT/UNIMPROVED No improvement

OBSOLETE Stat class code not in use, used in a prior year

Figure 8: Land use group codes

It was not difficult, in most cases, to assign land use codes to stat class codes. This is partly because the

land use group codes developed are very general. There were some ambiguous cases, however. For

example, parking structures may be of public use (COMMUNITY) or private use (COMMERCIAL). This

depends, not on the county in question, but on the particular parking structure. This is the sort of case

that reveals the problem with mapping a structure type to a structure use, as is common when using stat



class. One solution would be to refine the land use coding scheme to include more codes, such as to add

PARKING in this example.

LCOG staff chose not to refine the land use groups further for several reasons. These are listed below.

This is a “prototype” coding scheme, as such it is not prudent to invest too much into it at this

time.

A simpler scheme is more likely to work for all counties.

Use cases are not yet clearly identified. It can be refined if more detail is needed.

It needs local input to confirm land use assignments.

It will need more tuning once all counties are acquired.

It will need tuning annually as counties may change stat class codes.

Map Assessment Once the code comparison phase was completed, staff created maps of the stat-class-based land use

groups applied to the taxlots for each county. LCOG staff mapped several different areas of each county.

Countywide (various scales)

A rural focus area (1:16,000 scale)

A transitional focus area (1:4,000 scale)

An urban focus area (1:2,000 scale)

Where possible, the focus areas were chosen to show as many land uses as possible, but not all land

uses shown in the map legends are present on all maps. These maps can be found as Appendix A.

One significant hurdle in associating land uses to taxlots via the stat class codes is that these codes are

not usually found on the taxlots. They are found on the improvements table or the equivalent (e.g.

tables named “buildings,” or “real property”). This is primarily a structural data requirement since there

are potentially multiple tax accounts per tax lot and also potentially multiple improvements per tax

account. For this project, the primary improvement on the lot was identified where necessary. In some

counties the primary improvement was always listed first. In others, a rule had to be applied. The rule

used was to pick the most valuable improvement (in dollars per square foot) or the largest (in square

feet).

Deschutes – No multiple improvements; join was 1-to-1.

Harney – Multiple improvements per lot; joined on the 1st record which was the largest square

footage of the improvements.

Multnomah – Multiple improvements per lot; joined on the 1st record which was the largest

square footage of the improvements.

Josephine – Multiple improvements per lot; joined on the 1st record which was the highest

assessed value of the improvements.

Lane – Primary stat class already integrated into the parcel file.



In making the maps, the choice was made to exclude taxlots that the counties had identified as road

right-of-way or water. These are shown in white in the land use maps. In some cases, the air photos

show actual developed uses in those areas, so future refinement of this approach may be necessary.

However, this also reveals the need for a precision caveat when using taxlots to specify land use.

The maps also combine several data values under the VACANT/UNIMPROVED category. Lots with no

improvements (NULL for the land use group), those lots with improvements specified with a stat class

the relevant county considers obsolete, or those lots with a primary stat class that is defined by the

relevant county as unimproved.

Staff then assessed the maps for basic issues with the coding. This was not a complete and thorough

review, but was intended to identify the most obvious issues (e.g. categories of land not easily captured

in the land use groups). Generally, a recognizable land use pattern was generated. A quick comparison

with air photos yielded few surprises. Some land uses are not captured well by stat class however.

For example, for practical reasons, tax exempt improvements are not inventoried as regularly. So, there

are many places where land was coded as unimproved, but showed recent improvements. By

examination of the air photos and other assessor attributes such as owner name and prop class, most of

these were determined to be public uses (i.e. should have been coded as COMMUNITY). In some cases,

the air photo was simply more current than the assessor’s data. Another example is a large lot that is

mostly unimproved, but coded with the use of the primary improvement on the lot. One might argue

that the whole lot is not really being used for the same use as the primary structure. Finally, not all stat

class coding schemes make a clear distinction between the use of a structure and the type of structure.

As a result, the land use category becomes difficult to assign. These shortcomings are discussed more in

the conclusions below.



Conclusions Although there is not a strong, common standard in stat class coding across the state, it appears that

functionally similar stat class codes are common. Given a highly generalized land use coding scheme, it

was not difficult to assign land use codes to stat class codes. The review of data from assessor web sites

suggests that there are probably no more stat class coding scheme groupings to find. Any future

assignments of land use codes to the stat classes of counties acquired will be fairly easy since it will be

similar to those already done. The exception to this may be any other unique counties. However, the

assignment process was not really difficult in the case of any of the unique ones found so far,

particularly since the unique ones tend to be the most detailed. It is easier to assign a generalized coding

scheme to a detailed stat class scheme.

In addition to the variability in coding schemes that affects the comparison and assignment of land use

codes; there are a variety of formats and data structures used to represent the taxlot-improvement

relationship. Because there are potentially multiple tax accounts per tax lot and potentially multiple

improvements per account, assigning a land use value to a taxlot based on the stat classes of the

associated improvements is not a simple matter. For the current pilot study, some simplifying

assumptions and techniques are used, but a part of a statewide system that integrates stat class data

into a land use layer would have to be the development of a set of rules for each county to handle

ambiguous cases.

The most significant methodological limitation may be that the stat class codes only provide information

where there are improvements. For unimproved lots where more specificity is needed than simply

calling them “unimproved” (such as to differentiate between public and private open space or to

differentiate between different sorts of agriculture), supplemental data sources will need to be used.

The assessor’s prop class may provide an option in this regard. Although it represents the highest and

best use rather than the existing use, when there is no improvement the highest and best use and the

existing use are generally the same. Future studies could look at options for improvements in this

direction.

Even the perfect land use data at the parcel level could not do everything. Even where there is a stat

class on a lot and a single improvement, the land use of the whole lot, if very large, may be misleadingly

portrayed by using the use of the improvement to characterize it. For example, a 20 acre lot may have a

single family house on it and the rest may be in forest use. This is not a fatal flaw for the use of the data

for land use planning, but it does require that the data be used carefully. The above described lot would

not be considered vacant, but some large portion of it might be considered as part of the vacant land

inventory in a buildable lands study. Other supporting data would need to be used to make that

determination.

Overall, a common scheme of statewide land use codes based on stat class seems very feasible. It is

fairly easy to assign a general land use coding scheme to taxlots based on stat class information. There

are some hurdles to get over when it comes to data availability, particularly of the improvement data

which does not seem to be covered under current data sharing agreements (e.g. ORMAP) between most

of Oregon’s 36 counties and the State.



Next Steps A number of next steps are possible that could expand the approach studied here. A first step might be

to develop a plan and include estimates of work elements that might include the following.

Expand the assessment of stat class to include all counties (or at least those willing to

participate).

Refine the land use group coding scheme developed here to address DLCD and other state

agency use cases and develop it (or something similar) as a documented state standard. Involve

county assessment, GIS, and planning staff where possible.

Develop stat class aggregation rules for each county (for taxlots with multiple improvements).

Involve county assessment, GIS, and planning staff where possible.

Explore the possibility of supplementing stat class information with prop class information for

unimproved lots.

Analyze the quality of the land use layer derived from stat class (perhaps supplemented by prop

class) by comparing it to independent sources of information. These might include the following.

o Compare to addresses in Lane County.

o Compare to employment data in other counties.

o Work with staff in counties to do some “ground truthing.”

o Other possibilities, such as the following.

Google Street View images.

Bing oblique images.

Air photo analysis.

Develop a program to maintain this derived land use layer.

o One option would be to work with the State (DOR) to expand the ORMAP data sharing

agreement (currently only taxlots with prop class) to add the following.

Get participation from all counties.

Counties annually share improvement and code definition tables with DOR.

DOR annually shares this data with DLCD (directly or via GEO).

Explore long-range opportunities to integrate with other data efforts such as the state

addressing standard and the land cover standard.



Appendices This report includes the following appendices.

A. A set of sample maps for the selected project areas.

In addition, the following tables are appendices which, due to their size, cannot be delivered in the

current format and so are delivered in their original form (an Excel Workbook entitled Inventory.xlsx).

B. The inventory table from the Web Search and Acquisition phase

C. The code comparison table from the Code Assessment phase

D. The stat class lookup tables for each of the studied counties, including the corresponding

general land use groups assigned to them for comparison.

The final deliverable of this project also includes a digital delivery of all the data used for or created

during this project. This will include GIS data for each mapped county.2

2 Two counties (Deschutes, Harney) included terms and conditions documents with their data. These terms would

clearly prohibit LCOG from sharing the data with any party, including DLCD. Staff at these counties were contacted and they expressed that there was no concern with LCOG sharing the data with DLCD. Both counties asked that the terms and conditions document be included with the data sent to DLCD. Those documents will be found in the digital delivery.


Statewide Land Use - Stat Class Assessment - Report - FINAL.docx A-1

Appendix A: Maps

Deschutes A-2 County Land Use

A-3 County Air Photo

A-4 Rural Land Use

A-5 Rural Air Photo

A-6 Transitional Land Use

A-7 Transitional Air Photo

A-8 Urban Land Use

A-9 Urban Air Photo

Harney A-10 County Land Use


A-12 Rural Land Use

A-13 Rural Air Photo



A-16 Urban Land Use

A-17 Urban Air Photo

Josephine A-18 County Land Use


A-20 Rural Land Use




A-24 Urban Land Use


Lane A-26 County Land Use


A-28 Rural Land Use




A-32 Urban Land Use


Multnomah A-34 County Land Use


A-36 Rural Land Use




A-40 Urban Land Use


assessment of stat class applicability for building a ... documents/statewide...statewide land use -...

Documents