Our Covid-19 Plan:
We have a double room to allow for proper social distancing between students. However, because this is a classroom situation and for the health and safety of all present:
- Enrollment is Limited
- Masks Are Required whenever a student or assistant is within 6 feet of others
- Learner spacing will be two students per 8 ft table
- Instructor will be at least 16 ft from the front row
- Class assistants will be masked during any individual interaction
On the morning of class, should you feel sick or believe you may have been exposed to Covid-19 in the two weeks prior, please notify Cyndi at [email protected].
Stats, Graphs, and Data Science1 and 2 are a series of workshops that apply modern data science principles, predictive analysis, and evidence-based© methods to real estate valuation. These courses emphasize hands-on, activity-based learning using real data. A laptop is required.
In Stats, Graphs, and Data Science 1, students practice two basic analytical modeling tools: adjustment calculation using contrasting, and simple regression. Using real data sets, objective data selection is presented by the use of visual plots. Summary statistics in market selection and for adjustments is introduced.
These tools are particularly useful for sparse-data appraisal problems through estimation of location adjustments and price indexing of older data.
R, with the RStudio interface, (open-source analysis programs) are introduced and compared to traditional accounting spreadsheet add-ons. These programs are available at no charge. Assistance with download and installation are provided after registration if you need it. This class is taught using Gnumeric Portable spreadsheet software, R and RStudio.
Stats, Graphs, and Data Science 2, continues on the foundations of SGDS1. Building on the SGDS1 learning in depth, breadth, and detail. We continue examination of the theory, concepts, and practice of data science as applied to asset analytics. The learning follows the development sequence of valuation and risk analytics. Starting from the problem identification (scope of work) in measurable terms, we proceed to:
- Identify the overall project data frame;
- Define the CMS© (Competitive Market Segment), the ideal data set;
- Resolve the bias-variance tradeoff to optimize accuracy and precision of results;
- Perform necessary data wrangling (analogical to traditional confirmation/verification)
- Portray data visually
- Reduce to the CMS©
- Detect quantify missing/needed predictor variables
- Create new calculated fields
- Transform functional relationships (e.g., linear to logarithmic)
- Create Transformed data levels
- Identify and handle outliers
- Recognize needed predictive confirmation/verification
This class is substantially experienced in R, RStudio, and R packages, including ggplot2 and tidyverse. A comparison and integration of Excel® is provided for workflow efficiency. Methods of transition from canned appraisal software and the spreadsheet— to a real analytics package is reviewed.
Specific analytics include non-linear price indexing, seam regression, introduction to GIS in R, Data selection algorithms (particularly hierarchical cluster analysis), sources of help for R. Also resolution of conflicts in USPAP, AI-SVP, IVS, and EBV© (Evidence Based Valuation) practice, and reporting in a data-stream delivery through dashboard views.
In addition to George Dell, SRA, MAI, ASA, CRE, as your instructor –
Bruce Hahn, MAI, SRA, CRE, will be a second instructor in the class.
Bruce is an independent fee appraiser, applying R analytics to residential and other assignments.
- Hands-on Instruction
- Open-source spreadsheet and data analysis software
- State CE Certification for California and most other states (SGDS1)
- State CE Certification for SGDS2 for CA, WA, IL, OH, MI. Others pending.
- Student attendance certificate provided at the end of each class
Reference Book, Handouts, and Practice Data Sets.
Lunch served on the first full day of both classes (Tues & Thurs).