Spring 2027

Urban and Regional Economics

Focuses on the study of the allocation of scarce resources across space. Uses economic methods to analyze urban real estate. Topics covered include the determinants of real estate values, the location decisions of households and firms, land use, urban growth and agglomeration, real estate pricing, cycles, development, housing market and policies, and sustainable development.

offering recorded3 credits
Recorded instructors · Spring 2027 So Hye Yoon

Summary

1 / 6

Past student reviews · Stephen Malpezzi, Yongheng Deng

One student found the class very easy, while another reported some assignments were difficult. Instructors went over homework in class, which helped make those challenging tasks much easier to complete.

What you’ll be able to do

  • Explain the economic forces that drive urban growth and regional development.

  • Analyze how business and residential location decisions are made and how these decisions impact housing prices, land use, labor market, and many other aspects of cities.

  • Apply the spatial equilibrium concepts to urban development and real estate analysis.

  • Develop statistical models to assess residential and commercial real estate and perform sensible business and policy analysis.

  • Apply the economic decision-making framework to real estate development decisions.

  • Demonstrate understanding of cycles, risks and bubbles in residential and commercial real estate markets.

  • Evaluate the challenges in economic, social and environmental sustainability in urban development around the world.

  • Apply urban and regional economics to real estate business decisions and policy analysis.

Source: UW Guide 1 UW Guide 2 UW Guide 3

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

(ECON 101 or 111) or declared in undergraduate Business Exchange program

  • take one
ECON/REALEST/URBRPL 420 used by

“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027
Historical instructors & teaching patterns

STEPHEN MALPEZZI is recorded teaching in Spring 2010, Fall 2010, Fall 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016. Recorded history may be incomplete and does not establish a future schedule.

YONGHENG DENG is recorded teaching in Spring 2019, Spring 2020, Fall 2021, Fall 2022, Fall 2023, Fall 2024, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 002Classroom Instruction0 / 420
LEC 003Classroom Instruction0 / 400
LEC 005Classroom Instruction0 / 400
LEC 006Classroom Instruction0 / 400
LEC 002Classroom Instruction0 / 500
LEC 003Classroom Instruction0 / 500
LEC 005Classroom Instruction0 / 500
LEC 006Classroom Instruction0 / 400
LEC 002Classroom Instruction0 / 500
LEC 003Classroom Instruction0 / 400
LEC 005Classroom Instruction0 / 500
LEC 006Classroom Instruction0 / 400

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
Download part 1

Student experience

the class

Past student reviews · Stephen Malpezzi, Yongheng Deng

Past students described the course as engaging, with instructors who were knowledgeable and passionate about the material. Feedback noted that the teaching style focused heavily on learning strategies and reviewing homework in class.

Recent recorded grades — Spring 2025: 3.56 GPA, 78.2% A/AB (n=142 letter grades); Fall 2025: 3.52 GPA, 76.9% A/AB (n=160 letter grades); Spring 2026: 3.53 GPA, 74.3% A/AB (n=152 letter grades).

difficulty & workload

Past student reviews · Stephen Malpezzi, Yongheng Deng

One student found the class very easy, while another reported some assignments were difficult. Instructors went over homework in class, which helped make those challenging tasks much easier to complete.

Past student reviews · Stephen Malpezzi, Yongheng Deng

Students experienced varying levels of engagement depending on the instructor's mood and character. One student noted that a strong accent sometimes made it difficult to understand specific test questions.

Grades

Historical instructor

Spring 2027 · Projected

Before grades are released

– average GPA

Approximate 80% prediction interval

About this estimate

The course’s semester-average GPA, not an individual student’s grade. The center uses 5 same-season terms, weighted toward recent results.

The range uses the finite-sample 80th-percentile rank of absolute errors from earlier same-season forecasts. Each forecast uses only records from earlier terms. At least four forecasts are required; bounds are rounded outward and limited to 0–4. This is an empirical estimate: changing instructors or grading policies can reduce its coverage.

8 earlier forecasts · 0.15 GPA average error.

Grades over time

Through Spring 2027

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1280 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

Download part 1
Selected offering source records
ECON/REALEST/URBRPL 420 · Spring 2027

Urban and Regional Economics

Recorded 2026-10-14
ECON/REALEST/URBRPL 420 · Spring 2027

Urban and Regional Economics

Recorded 2026-10-14
ECON/REALEST/URBRPL 420 · Spring 2027

Urban and Regional Economics

Recorded 2026-10-14
Raw records 
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    "offering_id": "1274:239:003248",
    "course_id": "ECON/REALEST/URBRPL 420",
    "course_uid": "course_50007f8455de756ef2745b4c",
    "term_id": "1274",
    "source_course_id": "003248",
    "source_subject_id": "239",
    "title": "Urban and Regional Economics",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall"
  },
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    "offering_id": "1274:296:003248",
    "course_id": "ECON/REALEST/URBRPL 420",
    "course_uid": "course_50007f8455de756ef2745b4c",
    "term_id": "1274",
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    "source_subject_id": "296",
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    "credits_min": 3,
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    "typically_offered": "Fall"
  },
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:944:003248",
    "course_id": "ECON/REALEST/URBRPL 420",
    "course_uid": "course_50007f8455de756ef2745b4c",
    "term_id": "1274",
    "source_course_id": "003248",
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    "credits_min": 3,
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]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
Download part 1
Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "fe4f674e79b52677795b82a4569294363ab61855f7bd1ff16fb714532ee0761b",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}