lobbyTO

Registration SM24352 · consultant

Salford Investments Limited

Status: closed12 April 2017 to 30 June 2019Source datasetReport an error on this page
Communications
1
1 offices contacted
Lobbyists
1
Linked agenda items
0
Published links only
Linked applications
1
Published links only

Who

Client
Salford Investments Limited
Consultant firm
Goodmans LLP
Registrant
David Bronskill
Type
consultant
Status
closed
Dates
Approved 28 April 2017; 12 April 2017 to 30 June 2019

Subject matter

  • Planning and Development Application, Zoning By-law

Source: City of Toronto Lobbyist Registry (open data). Dataset page.

Particulars

33 Isabella Street -- Rezoning Application for Infill

Addresses named: 33 Isabella St (Ward 13)

Automated summary of the registration text

Registered, communicating about Rezoning Application for Infill at 33 Isabella Street

Generated from the registration text only. Read the particulars for the registrant's own words.

Lobbyists

Organisations named

Communications

Communications per month

2019-04 to 2019-04

1 communication

How and with whom

By method · 0 meetings

By office holder

Most-contacted offices

  • Office of Councillor Wong-Tam, Ward 13Councillor staff1
Offices contacted· 1 row
OfficeOffice holder typeCommunicationsMeetingsLast contact
Office of Councillor Wong-Tam, Ward 13Councillor staff1029 April 2019
All communications· 1 row
1 communications recorded
DateOfficeOffice holder (as recorded)Member's officeMethodLobbyist
29 April 2019Office of Councillor Wong-Tam, Ward 13Edward LaRusic
Councillor staff
—Email
E-mail
—

"Member's office" is the member of council whose office was contacted (the member or their staff), resolved from the office holder name and office recorded in the registry.

Linked agenda items

No published links to agenda items.

Linked development applications

These links are inferred by lobbyTO from file numbers and addresses named in the registration (task L2); the registry does not record which application a registration concerns.

Links between records are inferred by a model and are not reviewed by a person. Each link shows its score out of 1; moderate-confidence links (0.7 to 0.9) are wrong more often than high-confidence links. How links are scored