Three Bedroom Home Prices in St. Catharines and Thorold

135 Taylor Rd, Niagara-on-the-Lake, ON L0S 1J0
T - (905) 341-2393 | E – macdonaldcr4@gmail.com
Selling Prices for Three Bedroom
Detached Homes in the
St. Catharines-Thorold Area
GISC9308 – Spatial Analysis Statistics
Prepared For: Ian Smith
Prepared By: Chris MacDonald and James Stavridis

March 16, 2016
GISC9308D4B
Ian Smith, B.Math.,M.Sc., O.L.I.P.,O.L.S,EP
CampusFacilitiesManagementSupervisor
135 TaylorRoad
Niagara-on-the-Lake,ON L0S1J0
Dear Mr. Smith:
Re: GISC9308 Deliverable 4b – Pricesof Three BedroomDetached Homes in St. Catharinesand Thorold
Please acceptthisletteras aformal submissionforDeliverable D4b– Pricesof Three BedroomDetachedHomes
in St. Catharines and Thorold.
The attached report outlines the methodology, procedures and analysis for the approved 100 points data
collection of three bedroom,detachedhouseprices withinthe St.CatharinesandThoroldarea.The collectionof
this data assisted in the development of two predictive surfaces created using the Inverse Distance Weighted
and Kriging interpolation methods. Both surfaces were created to determine the predicted value of a three
bedroom house in the St. Catharinesand Thorold area. It was determined that Kriging was more accurate and
effective in predicting the value of a three bedroom detached house within the selected study area.
If you have any questionsregardingthe enclosedproposal orrequire anymore information,please contactme
at yourearliestconvenience viaphone at(905) 341-2393 or by email at macdonaldcr4@gmail.com. Thankyou
in advance for you time and attention. I look forward to hearing your feedback on this project.
BestRegards,
ChrisMacDonald,BBA
ProjectManager
GIS-GMCertificate Candidate
CM/
Enclosures: 1. Report- SellingPricesforThree BedroomDetachedHomesinthe St.Catharines&Thorold
Area.
Cc: JamesStavridis
GIS-GMCandidate
GIS Analyst

Selling Prices for Three Bedroom Detached Homes in the St. Catharines & Thorold Area March 16, 2016
i | P a g e Project ID: GISC9308D4B
Contents
1. Introduction .......................................................................................................................................1
2. Study Area ..........................................................................................................................................1
3. Data Collection...................................................................................................................................2
4. Data Analysis of Collected Points.......................................................................................................4
5. Creation of Predictive Surfaces..........................................................................................................8
5.1. Inverse Distance Weighting Method ..............................................................................................8
5.2. Kriging Method ...............................................................................................................................8
6. Methodology......................................................................................................................................9
6.1. Inverse Distance Weighting Technique ..........................................................................................9
6.2. Kriging Technique..........................................................................................................................11
7. Overall Data Analysis and Finding....................................................................................................15
7.1. Evaluation of Sample Coverage ....................................................................................................15
7.2. IDW Analysis..................................................................................................................................16
7.3. Kriging Analysis .............................................................................................................................18
7.4. Comparison of IDW and Kriging....................................................................................................20
7.4.1. Interpolated Data with Reality..................................................................................................20
7.4.2. Best Model for Predicting Housing Prices.................................................................................22
8. Conclusion & Recommendations.....................................................................................................23
Bibliography .............................................................................................................................................24
APPENDIX A – Example of Data Table
List of Figures
Figure 1 - Study area map showing St. Catharines and Thorold in Niagara Region. .................................2
Figure 2 - Limiting features from Realtor.ca website used for data collection. ........................................3
Figure 3 – Final results of Data points from Realtor.ca website with all variables selected.....................4
Figure 4 - Map showing the sample house selling price points and overall average house location.......5
Figure 5 - Histogram of Price frequency. ...................................................................................................6
Figure 6 - Boxplot of the houses selling prices, showing the outliers in the data.....................................6
Figure 7 - Distribution of Easting points within the data...........................................................................7
Figure 8 - Distribution of Northing points within the data........................................................................7
Figure 9: IDW Method Properties..............................................................................................................9
Figure 10: Cross Validation, IDW Method. ..............................................................................................10

ii | P a g e Project ID: GISC9308D4B
Figure 11: Classification, IDW Method ....................................................................................................11
Figure 12: QQPlot of the data, showing an exponential distribution......................................................12
Figure 13: Semivariogram/Covariance Modeling, Kriging Method .........................................................13
Figure 14: Searching Neighbourhood, Kriging Method ...........................................................................14
Figure 15: Cross Validation, Kriging Method ...........................................................................................14
Figure 16: Method Report, Kriging Method ............................................................................................15
Figure 17 - Inverse Distance Weighting Analysis Map.............................................................................17
Figure 18 - Final kriging analysis map......................................................................................................19
List of Tables
Table 1. Single Detached Houses within Study Area .................................................................................3
Table 2 - Mean, median and standard deviation table for depths, and average Easting and Northing
locations.....................................................................................................................................................5
Table 3 - Predicted Cost vs Actual Cost IDW Method..............................................................................22
Table 4 - Predicted Cost vs Actual Cost, Kriging Method.........................................................................22

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1. Introduction
There are many familiar expressions in real estate.Location, location, location, alsoknown as the three
rules of real estate, is one of the most often heard idioms. While this expression has been derived from
sound principles that had been noticed over time, it can be over simplified and requires further
investigation (Mathers, 2013). The Niagara region remains friendly for cost-conscious home buyers
compared to the ever increasing real estate prices in Toronto and Vancouver. According to the Niagara
Association of Realtors, the average resale home price in Niagara for 2014 was $251,531.
The purpose of this assignment is to report upon the collection of geospatial housing price data. CM
Geomatics will describe in detail the data in both geostatistical and practical terms to demonstrate a
clear understanding of the data’s extent and validity. In an effort to geospatiallypredict housing prices
within the NiagaraRegion, the team has collected 100 housing price points from the St. Catharines and
Thorold areas. In addition, the Easting and Northing of these locations have been collected to predict
the geospatial coverage of these prices. From the dataset, the team has analyzed and created two
surfaces for predicting the price of three bedroom detached houses within the St. Catharines and
Thorold area.
2. Study Area
The study area for this project included the cities of St. Catharines and Thorold in the Niagara Region.
The rationale for choosing this area of study was the proximity of the cities to each other and the
availability of data retrieval. It is important for the study area to follow to a rectangular shape, as the
shape of a study area can affect the accuracy and estimate of spatial patterns. Refer to Figure 1, which
shows the study area within a map of the Niagara Region.

Figure 1 - Study area map showing St. Catharines and Thorold in Niagara Region.
3. Data Collection
On the week of January 24, 2016, a sample of 100 houses for salewithin the St. Catharines and Thorold
area was retrieved from the Realtor realestate website. The teamconsiders the data from this website
to be trustworthy, accurate and reliable for current real estate listings of residential properties.
A sample size calculator was used to calculate the sample size required for the study. According to
Statistics Canada,the total number of singledetached houses in the study areawas found to be 37,310
(Refer to Table 1).

Table 1. Single Detached Houses within Study Area
Single Detached
Houses
% of Population
St. Catharines 32,230 58.2%
Thorold 5,080 71.7%
Total Single Detached Houses in Study Area 37,310
At an acceptable +/-10% margin of error tolerance and 95% confidence level, the minimum
recommended sample size of the survey was determined to be 96 houses. With a collected sample
total of 100 houses, this improves the margin of error to +/-9.79.
Using the filtering options within the website, the data were limited to residential, detached, three
bedroom houses valued over $100,000. All data was recorded using a Microsoft Excel worksheet.
Figure 2 below displays a screen capture of the Realtor website’s menu screen with all appropriate
variables selected.
Figure 2 - Limiting features from Realtor.ca website used for data collection.
Figure 3 shows the final map of collected points, as viewed when the variables were selected from the
website.

Figure 3 – Final results of Data points from Realtor.ca website with all variables selected.
Two team members retrieved the information on the same day, in order to accurately obtain all the
data from the website. For tracking purposes, all collected points were assigned their own unique
identifier. Other key data collected included the Listing ID,the listing address which includes the Street
Number, Street Name, City, Province and Postal Code. Spatial references were also added, including
the easting and northing location of the houses, the UTM zone and the latitude and longitude of the
house location. Lastly, the selling price of the house was collected for each listing. The table in
Appendix A shows an example of the data collected within the excel document. The Easting data will
represent the X value the Northing data will represent the Y value and the Price will represent the Z
value. To ensure accuracy, the excel document was proofread by both members, eliminating
duplicates. Alleasting and northing points, addresses and prices were double-checked to minimize the
error.
4. Data Analysis of Collected Points
The collection of 100 unique data points was analyzed in both Microsoft Excel and R software
programs. The dependent variable within the dataset will be the selling prices for each of the 100
houses within the study area. Theseprices range from the lowest price of $109,000 to the highest price
of $899,000. Table 2 below shows the mean price, median price and the standard deviation of the
dataset.

Table 2 - Mean, median and standard deviation table for depths, and average Easting and Northing locations.
Mean $332,824
Median $282,450
Standard Deviation $171,699
Minimum Price $109,000
Maximum Price $899,000
Average Easting Location 643643
Average Northing Location 4736112
Also shown are the average easting and northing locations. This means that the average house will be
located at easting 643643 and northing 4736112, with an average price of $332,824. Each sample
location was plotted on a map in ArcGIS 10.3. Figure 4 shows these data points from the study area
map, as well as the average location within the study area.
Figure 4 - Map showing the sample house selling price points and overall average house location.

As shown in Figure 5, a histogram was created to show the frequency distribution of the prices for the
100 houses in the study region.
Figure 5 - Histogram of Price frequency.
The data are skewed to the right and there is a declining frequency relationship between the prices,
from the lowest prices to the highest prices. The majority of housing prices fall between $200,000 and
$400,000.
A boxplot was created to find outliers in the upper and lower quartiles within the data (please refer to
Figure 6).
Figure 6 - Boxplot of the houses selling prices, showing the outliers in the data.

The boxplot shows that any price over $650,000 is an outlier. Therefore, as a result, a total of six
houses are considered outliers from the overall sample data collected.
Two boxplots were also created to show the distribution of easting (or X points) and northing (or y
points) as seen in Figure 7 and Figure 8.
Figure 7 - Distribution of Easting points within the data.
Figure 8 - Distribution of Northing points within the data.
In the boxplot of easting points, the point locations show the average to be a higher number, meaning
that the locations tend to be more to the east, with a few outliers in the west. The northing boxplot
shows the data to be much higher to the north, within St. Catharines and the northern part of Thorold,
with a few outliers south of Thorold.

5. Creation of Predictive Surfaces
The team created two predictive surfaces with Inverse Distance Weighted and Kriging interpolation
methods. From the data collected, it was determined which method was more accurate at predicting
the actual selling price of a three bedroom house in any particular area of the St. Catharines and
Thorold area. Based on the Root Mean Square (RMS) error, both methods of interpolation were examined
for accuracy andeffectiveness. ArcGIS Geostatistical Analyst was utilized to extrapolate and analyze the
housing price data.
5.1. Inverse Distance Weighting Method
The Inverse Distance Weighted (IDW) is a method for predicting price in locations that have not been
surveyed to create a surface. Cell values of IDW-surfaces are estimated by averaging the values of
sample data points in the neighborhood of each processing cell (ESRI, 2015). Inverse Distance
Weighting assigns a higher weight of importance to values close to an estimated area. Values closest
to the center of a point of interest are considered more similar than those farther away. The weight
assigned to the relationship decreases as the distance between known values and estimated area
increases.
The Inverse Distance Weighted method is a good predictive method to use for housing prices, as
proximity directly affects housing value. The probability is higher that the value of neighbouring
houses will be similar in price than houses farther away. Therefore it would be logical to assign a
heavier weight to known prices located closer to the unknown location. There are some disadvantages
to this technique; particularly IDW’s dependence of distance. If the data is too spread out, the result
will be less accurate. In addition, IDW cannot be adjusted for any directional trends (ESRI, 2015).
5.2. Kriging Method
The Kriging method is another method used to predict prices of a study area based on a given dataset.
Similar to the IDW method of interpolation, Kriging estimates an unknown value based on the
attributes of its surroundings. Similar to the IDW method, Kriging allows for further modification and
customization. Kriging is described as a “multi-step process” which can adjust for any directional
trends that exist within the data (ESRI, 2015). Unlike the IDW method, Kriging takes in consideration
not only the distance between points, but also the spatial arrangement of those points (ESRI, 2015)

6. Methodology
6.1. Inverse Distance Weighting Technique
To perform an Inverse Distance Weighting (IDW), the Geostatistical Wizard was selected in ArcMap.
The IDW tool was selected under the Deterministic Method option. The Source Dataset was selected
as Houses for Sale from the created shapefile and Price was selected as the Data Field. Since the
selection of neighbours is dependent on the spread of the data, the minimum neighbour was set to 3.
This number was determined an appropriate setting because the southwest Thorold section of the
study area only consisted of relatively few points. This allows for predictions to be made even if the
areas are significantly farther apart from the main clusters in the dataset. The maximum neighbour
used for the prediction was 25, as Central St. Catharines had a significant cluster of data points. The
maximum number of points was determined based on the RMS error and this number produced the
lowest RMS value. The method properties are shown in Figure 9 below.
Figure 9: IDW Method Properties
The results were evaluated for accuracy and overall effectiveness of the result. A variety of statistics
can be used to measure the accuracy of the general predictive surface. The cross validation
comparison was used to analyze the Root Mean Square Error. A lower RMS error means a more
accurate predicted price. The RMS error for the IDW interpolation of house selling prices was

10 | P a g e Project ID: GISC9308D4B
$156,202.90, which shows that for every predicted price for a three bedroom, detached house in the
study area will be +/- $156,202.90 from that given value. A screen capture of the cross validation of
the data are shown in Figure 10.
Figure 10: Cross Validation, IDW Method.
The next step was to select a classification method. The method used in the classification was
Geometric Interval and 8 Classes were selected. Please refer to section 7.2 IDW Analysis Final Map.
The histogram in Figure 11 below displays the settings used in this step.

Figure 11: Classification, IDW Method
6.2. Kriging Technique
The dataset was analyzed before executing the Kriging method to determine possible trends that may
skew the results. Trends may have to be removed or corrected to produce a more accurate result. For
this project, trends are analyzed though the Trends Analyst tool within the Explore Data option in the
Geostatistical Analyst extension in ArcGIS.
The trend analysis shows an exponential distribution which can be seen in the QQPlot in Figure 12
below.

Figure 12: QQPlot of the data, showing an exponential distribution.
To begin the Kriging analysis, the Geostatistical Wizard function was selected and Kriging/CoKriging
was selected under the Geostatistical Methods. For the Under Source Dataset option, Houses for Sale
was selected and for the Data Field option, Price was selected.
For the next step, the Use Mean was selected by default and the Kriging type chosen was Ordinary.
This technique was used as the Universal type would require mathematical justification for removing
trends within the dataset (ESRI, 2015). The Transformation Type and Order of Trend Removal were
left None, as the default. Prediction was selected as the Output Surface Type. As shown in Figure 13,
the Semivariogram of the model is fairly level overall, demonstrating that the sample locations are
more spatially autocorrelated throughout the dataset.

Figure 13: Semivariogram/Covariance Modeling, Kriging Method
As locations become farther from the prediction location, the collected values have less spatial
autocorrelation with the prediction location. By defining a search neighborhood, these points can be
eliminated from the calculation of that particular prediction point, as they have little or no effect on
the predicted value. Kriging uses the data configuration specified by the search neighborhood in
conjunction with the fitted semivariogram model. It was determined this method required a more
precise minimum and maximum neighbourhood to better predict the value for a given location. The
parameters are displayed in Figure 14.

Figure 14: Searching Neighbourhood, Kriging Method
Figure 15 shows the RMS values of the prediction errors. RMS error is $161,769
Figure 15: Cross Validation, Kriging Method

Method Report of the Kriging Method shown below in Figure 16.
Figure 16: Method Report, Kriging Method
To keep the kriging model more consistent with the IDW method, geometric interval and eight classes
were selected for the classification.
7. Overall Data Analysis and Finding
7.1. Evaluation of Sample Coverage
Like the team mentioned above, there are certain areas within the study area that do not have any
houses that were for sale. These areas include the south area of Thorold and the west area of St.
Catharines. As the sample size was limited to 100 points of data to maintain a +/-10% margin of error
tolerance, the team had to go outside the study area to acquire the required amount of data, which
created gaps in the study area. In addition, the outskirts of the city contain more agricultural areas,
which do not have many houses on these areas. If the team wanted to perform an improved analysis
of the housing prices in St. Catharines and Thorold, an increased number of house selling prices would

be needed to be collected, or property values, to better determine the predicted cost of houses within
St. Catharines and Thorold.
7.2. IDW Analysis
The IDW analysis showed the predicted areas that would have the cheapest selling prices would be
central St. Catharines, along the highway and downtown Thorold. The most expensive predicted price
areas were given to be west St. Catharines, north St. Catharines along Lake Ontario and along Twelve
Mile Creek. The predicted pricing areas seemed to be more scattered throughout the study area.
Please refer to Figure 17 for the final IDW analysis map.

Figure 17 - Inverse Distance Weighting Analysis Map

7.3. Kriging Analysis
The Kriging technique showed more gradual increasing bands, outwards from the cheapest predicted
price area of central St. Catharines, to the highest predicted pricing areas of the outskirts. Please
refer to Figure 18 for the final kriging analysis map.

Figure 18 - Final kriging analysis map

7.4. Comparison of IDW and Kriging
7.4.1. Interpolated Data with Reality
To test the accuracy and effectiveness of the two predictive surfaces, a comparison of the final
results were made to new pricing information on housing prices in the study area. Overall, a visual
analysis showed prices are typically lower in the in central St. Catharines, especially along the Queen
Elizabeth Way. The houses that typically had a higher selling price were in outskirts of the study
area, especially along Lake Ontario and the bordering area of Lincoln and Pelham.
Four addresses were randomly selected throughout St. Catharines and Thorold using Realtor website.
These houses were not listed from the original searched housing prices on the week of January 24th.
Figure 1 shows where these locations are on the map. These address where used to see whether the
selling price was within our predicted ranges for both the IDW analysis and the Kriging analysis to
determine if our predictions were accurate.

The predicated cost for each point was found by first creating a new layer with the five new address.
To acquire the predicted prices, the geostatistical wizard was used for both the IDW and the kriging
techniques. The latitude and longitudes of the real world locations were inputted, which would then
show the predicted amount of that area, using the parameters stated above. This can be seen in Table
3, which represents the IDW analysis andTable 4, which represents the Kriging analysis,comparing the
predicted price to the actual selling price of the home.
Table 3 - Predicted Cost vs Actual Cost IDW Method
ADDRESS CITY PREDICTED
PRICE
ACTUAL
PRICE
Difference
268 GranthamAvenue St. Catharines $236,466 $184,900 $51,566
188 RykertStreet St. Catharines $324,626 $549,000 -$224,374
9 CulliganCrescent Thorold $368,934 $289,900 $79,034
41 McDonald Avenue Thorold $215,414 $214,900 $514
61 WildwoodRoad St. Catharines $268,414 $379,500 -$111,086
Overall Average Difference -$40,869.20
Table 4 - Predicted Cost vs Actual Cost, Kriging Method
ADDRESS CITY PREDICTED
PRICE
ACTUAL
PRICE
Difference
268 GranthamAvenue St. Catharines $228,944 $184,900 $44,044
188 RykertStreet St. Catharines $392,571 $549,000 -$156,429
9 CulliganCrescent Thorold $320,961 $289,900 $31,061
41 McDonald Avenue Thorold $263,961 $214,900 $49,061
61 WildwoodRoad St. Catharines $388,276 $379,500 $8,776
Overall Average Difference -$4,697.40
7.4.2. Best Model for Predicting Housing Prices
Basedon the predicted cost versus the actualprice of the fivereal world locations,it can be determined
that the Kriging technique was the best and most appropriate model for predicting housing prices in
St. Catharines and Thorold. This can be seen with the overall average difference of the kriging method
only being $4,700 while the difference for the IDW method was over $40,000. In fact, only the house
at 41 McDonald Avenue had a closer predicted price using the IDW technique over the Kriging
technique. The IDW cannot adjust for directional trends, whereas the kriging method factors in the
distance between points and the spatial arrangement of these points. The extended parameters
allowed in the kriging technique reveals that the predicted price was able to do more calculations to
garner the final number.
Considering the RMS error, both the inverse distance weighting and kriging technique had a relatively
low error amount, which meant that both technique were considered to be fairly accurate. However,
the predicted price versus the actual price showed a wide range of accuracy. The house at 188 Rykert
Street had the most inaccurate prediction due to its location on the outskirts of the study area. There
was less data in this area, so the prediction method was unable to grab the maximum amount of

neighbours in order to properly predict. This could also mean that the RMS error was more prevalent
in the areas with less housing data. Again to overcome this inaccurate prediction, more data would
have to be used especially in these outskirt areas, to minimize the effect of the RMS errors on the
prediction methods.
The predictive area is also using every known location in the study area, which does not properly
represent real life. This study area include bodies of water, roads, agricultural land and commercial
buildings. Spatially, these areas should not be calculated as part of the prediction method as they
would not have any houses for sale, therefore giving a better predicated result for areas with houses
for sale.
8. Conclusion & Recommendations
Several recommendations are suggested from the development of two predictive surfaces created
using the Inverse Distance Weighted and Kriging interpolation methods. It has been determined the
predicted listing price of a three bedroom house in the St. Catharines and Thorold area. The method
that gave the most accurate and effective in predicting the listing price of a three bedroom detached
house within the study area. The kriging method was considered to be a more accurate prediction
technique over the IDW method, given the lower average difference of our five real world comparison
houses. However, given the low RMS errors on both techniques, they were both considered to be
fairly accurate in predicting housing prices in St. Catharines and Thorold areas.
A simple recommendation would be to gather more housing prices within the St. Catharines and
Thorold area. This would give a more precise prediction outcome for both the IDW and the kriging
method, as it would be utilizing more data.
It would be expected that a topological map of city with real estate values would tend to peak at the
city's center (downtown) then decline as we move away. The overall trend is a high concentration of
values that tapers off to the borders (Mathers, 2013). However, the study area in this case gave the
opposite result, as several other factors such as economy, population, and the not a major
metropolitan centre but rather a suburban area.

Bibliography
Babish,G. (2006). GeostatisticsWithoutTears:A practical guide to surface interpolation,geostatistics,
variogramsandkriging.Regina:EnvironmentCanada.
ESRI. (2016). ArcGIS 10.3 Help.Redlands,CA:ESRI.
Mathers,W. (2013). LocationValue. RetrievedonMarch 17, 2016 from
http://www.realmarkits.com/realestate/locationvalue.php
NiagaraAssociationof Realtors(2015).Retrieved onMarch 13, 2016 fromhttps://www.niagararealtor.ca
NIAGARA CLOSEUP:From Toronto,withmoney.Retrieved onJune 18,2015 fromSt. CatharinesStandard:
http://www.stcatharinesstandard.ca/2015/06/18/niagara-closeup-from-toronto-with-money
Smith,I.D. (2016). Termsof Reference:Geostatistical Analysisof StudentCollectedSpatial Data.Niagara
College.Niagara-on-the-Lake,Ontario.
StatisticsCanada.2012. Focus on GeographySeries,2011 Census.StatisticsCanadaCatalogue no.98-310-
XWE2011004. Ottawa,Ontario.Analytical products,2011 Census.

APPENDIX A – Example of Data Table
Point ID Listing ID Address City Province Postal Code UTM Zone Easting Northing Price
1 N30052472 9 Colborne Street Thorold Ontario L2V 3P6 17 645844 4775966 159900
2 N30054639 58 St. David Street West Thorold Ontario L2V 2L5 17 645757 4776769 165000
3 N30055132 18 Ormond Street South Thorold Ontario L2V 1Y1 17 646518 4776168 169900
4 N30054893 111 Pine Street Thorold Ontario L2V 3M2 17 646224 4775370 174500
5 N30053488 30 St. David Street Thorold Ontario L2V 2L2 17 646045 4776760 189900
6 N30054543 34 Water Street Thorold Ontario L2V 2K4 17 645816 4776608 209000
7 N30055023 29 Chapel Street South Thorold Ontario L2V 2C7 17 646780 4776075 209900
8 N30047553 58 Maitland Street South Thorold Ontario L2V 3A9 17 645656 4775842 217900
9 N30054196 21 Carleton Street South Thorold Ontario L2V 1Z7 17 646596 4776041 235000
10 N30053472 22 Wanda Road St. Catharines Ontario L2T 1S4 17 645954 4777081 284900
11 N30054569 16 Thompson Avenue Thorold Ontario L2V 2M8 17 645725 4776120 242900
12 N30054470 5 Hillgarden Road St. Catharines Ontario L2T 2W7 17 645023 4776524 309900
13 N30055066 86 Pine Street North Thorold Ontario L2V 2P4 17 646146 4776899 329900
14 N30054905 252 Winterberry Boulevard Thorold Ontario L2V 5E5 17 643732 4775010 329900
15 X3301182 61 Raspberry Trail Thorold Ontario L2V 5E2 17 643900 4774955 349900
16 N30055119 254 Confederation Avenue Thorold Ontario L2V 5A2 17 644447 4774952 364900
17 N30054503 13 Abbey Drive Thorold Ontario L2V 4S9 17 644369 4774248 379900
18 N30053330 10 Dobbie Road Thorold Ontario L2V 4R7 17 644457 4774252 389900
19 N30048443 6 Beamer Court Thorold Ontario L2V 4K7 17 645206 4774504 399900
20 H3172864 67 Marsdale Drive St. Catharines Ontario L2T 2B5 17 642882 4776814 399900
21 N30054160 166 Riverview Boulevard St. Catharines Ontario L2T 3M7 17 642450 4776842 564900
22 N30053527 199 St. Davids Street West Thorold Ontario L2V 2M2 17 646009 4776755 899000
23 N30049049 31 Walnut Street St. Catharines Ontario L2J 1H5 17 645382 4777974 143900
24 N30055082 162 Oakdale Avenue St. Catharines Ontario L2P 2K9 17 644612 4778858 164900
25 N30054683 4 David Street St. Catharines Ontario L2S 1O2 17 641971 4777290 143900
26 N30053899 15 Chetwood Street St. Catharines Ontario L2S 1J9 17 642356 4778382 159900
27 N30054504 24 Edith Street St. Catharines Ontario L2S 2P7 17 641581 4778275 160000
28 N30055095 104 Pelham Road St. Catharines Ontario L2S 1T2 17 642186 4777852 174900
29 N30054790 260 Vansickle Road St. Catharines Ontario L2S 2S8 17 640701 4777807 224900
30 N30052530 283 Vansickle Road St. Catharines Ontario L2S 2T3 17 640642 4778053 289900
31 X3283915 14 Cozocar Crescent St. Catharines Ontario L2S 3Y5 17 640819 4776976 325000
32 H3173125 163 Glendale Avenue St. Catharines Ontario L2T 2K2 17 643660 4777215 339900
33 X3381239 101 Westland Street St. Catharines Ontario L2S 3X6 17 640960 4777108 364900
34 N30055074 58 Village Road St. Catharines Ontario L2T 3B7 17 643813 4777851 419900
35 N30054175 131 South Drive St. Catharines Ontario L2R 4W1 17 643286 4778671 389900
36 N30053892 74 Village Drive St. Catharines Ontario L2T 3B7 17 643843 4777678 439900
37 N30054802 33 Westland Street St. Catharines Ontario L2S 3W7 17 641004 4776578 479900
38 N30053963 4 Rodman Hall Drive St. Catharines Ontario L2S 1P2 17 642502 4778911 499000
39 N30053773 13 Damson Street St. Catharines Ontario L2S 4B2 17 641202 4777116 524900
40 N30054743 535 Geneva Street St. Catharines Ontario L2N 2H8 17 642830 4784116 231000
41 N30055062 34 Harcove Street St. Catharines Ontario L2N 1X2 17 643417 473572 235000
42 H3173947 30 Highcourt Crescent St. Catharines Ontario L2M3M6 17 643920 4783458 259999
43 N30054512 68 Else Street St. Catharines Ontario L2N 2C3 17 643086 4783841 264900
44 N30053098 2 Highcourt Crescent St. Catharines Ontario L2M3M6 17 643867 4783671 280000
45 N30054256 8 Trinity Circle St. Catharines Ontario L2N 2K8 17 642676 4783028 295000
46 N30052370 81 Else Street St. Catharines Ontario L2N 2C4 17 643060 4783699 304900
47 N30054715 557 1/2 Geneva Street St. Catharines Ontario L2N 2J1 17 642805 4784373 309900
48 N30052793 12 Missassauga Road St. Catharines Ontario L2N 3K2 17 643328 4785954 374900
49 H3172574 22 Northglen Avenue St. Catharines Ontario L2M1B4 17 644584 4786931 229000
50 N30054595 37 Cumberland Street St. Catharines Ontario L2M1E4 17 644734 4787041 369500
51 N30055047 31 Glenellen Drive St. Catharines Ontario L2M5Y6 17 645338 4782658 312000
52 X3352733 41 Jessica Drive St. Catharines Ontario L2M6V9 17 644475 4784545 349900
53 N30053779 11 Shade Tree Crescent St. Catharines Ontario L2M7G3 17 644797 4783508 359900
54 N30054408 9 Regina Avenue St. Catharines Ontario L2M3G5 17 644641 4784038 389000
55 X3372797 10 Verdun Avenue St. Catharines Ontario L2N5L9 17 640086 4783944 274900
56 N30054885 37 The Meadows St. Catharines Ontario L2N 7K8 17 641897 4783086 379500
57 N30054777 61 Bayview Drive St. Catharines Ontario L2N 4Y5 17 640067 4784400 524900
58 N30053290 19 Lanark Crescent St. Catharines Ontario L2M6E1 17 646034 4782592 249900
59 N30053374 23 Dunblane Avenue St. Catharines Ontario L2M4Z8 17 645926 4783571 239900

Three Bedroom Home Prices in St. Catharines and Thorold

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