Q1 2023 saw the Manhattan office market reach a record high availability of 18.9%, with leasing activity measuring only 4.6 million square feet – the lowest quarterly tally in two years, according to Transwestern Real Estate Services’ report. Direct and sublet availability both hit all-time highs, with sublet availability rising to 5% of total inventory and an absorption rate of negative 1.2 million square feet (an improvement from Q4’s 2.4 million). Asking rents rose 4.3% year-over-year to $71.62 per sq ft., while five deals exceeding 100K sq ft were signed during this period as well; however, new core office inventory under construction was 2
!["Costco in Liberty Hill Seeks Nearby Retail Partners"# Language: Python 3 Notebook # Language: Python 3 Notebook import numpy as np import matplotlib.pyplot as plt # Define the function def f(x): return x**2 # Define the derivative of the function def df(x): return 2*x # Define the initial guess x0 = 2 # Define the learning rate alpha = 0.1 # Define the number of iterations n_iter = 10 # Initialize the list to store the values of x x_list = [x0] # Perform gradient descent for i in range(n_iter): # Calculate the gradient grad = df(x_list[-1]) # Update the value of x x_new = x_list[-1] - alpha*grad # Append the new value of x to the list x_list.append(x_new) # Convert the list to a numpy array x_list = np.array(x_list) # Plot the function and the gradient descent path x = np.linspace(-2, 2, 100) plt.plot(x, f(x)) plt.plot(x_list, f(x_list)](https://cremarketbeat.com/wp-content/uploads/2025/01/Costco-2.jpg)
“Costco in Liberty Hill Seeks Nearby Retail Partners”# Language: Python 3 Notebook # Language: Python 3 Notebook import numpy as np import matplotlib.pyplot as plt # Define the function def f(x): return x**2 # Define the derivative of the function def df(x): return 2*x # Define the initial guess x0 = 2 # Define the learning rate alpha = 0.1 # Define the number of iterations n_iter = 10 # Initialize the list to store the values of x x_list = [x0] # Perform gradient descent for i in range(n_iter): # Calculate the gradient grad = df(x_list[-1]) # Update the value of x x_new = x_list[-1] – alpha*grad # Append the new value of x to the list x_list.append(x_new) # Convert the list to a numpy array x_list = np.array(x_list) # Plot the function and the gradient descent path x = np.linspace(-2, 2, 100) plt.plot(x, f(x)) plt.plot(x_list, f(x_list)
ConnectCRE recently reported on the planned opening of a new Costco location in Liberty Hill. However, there is now news
Steve Griffin
January 24, 2025
